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<H1>Association Rule Mining - Annodated Bibliography by Michael Hahsler</H1><table>

<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Kohavi:1988">1</a>]
</td>
<td class="bibtexitem">
Ron Kohavi and Foster Provost.
 Glossary of terms.
 <em>Machine Learning</em>, 30(2--3):271--274, 1988.
[&nbsp;<a href="association_rules_bib.html#arules:Kohavi:1988">bib</a>&nbsp;]
<blockquote><font size="-1">
Definition of measures from Machine Learning. Theses are especially
	interesting for comparison and evaluation of association rule algorithms.
</font></blockquote>
<blockquote><font size="-1">
Keywords: evaluation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Sebag:1988">2</a>]
</td>
<td class="bibtexitem">
M.&nbsp;Sebag and M.&nbsp;Schoenauer.
 Generation of rules with certainty and confidence factors from
  incomplete and incoherent learning bases.
 In <em>In Proceedings of the European Knowledge Acquisition Workshop
  (EKAW'88), Gesellschaft fuer Mathematik und Datenverarbeitung mbH</em>, 1988.
[&nbsp;<a href="association_rules_bib.html#arules:Sebag:1988">bib</a>&nbsp;]
<blockquote><font size="-1">
Defines the Seebag measure for rules.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Piatetsky-Shapiro:1991">3</a>]
</td>
<td class="bibtexitem">
G.&nbsp;Piatetsky-Shapiro.
 Discovery, analysis, and presentation of strong rules.
 In G.&nbsp;Piatetsky-Shapiro and W.J. Frawley, editors, <em>Knowledge
  Discovery in Databases</em>. AAAI/MIT Press, Cambridge, MA, 1991.
[&nbsp;<a href="association_rules_bib.html#arules:Piatetsky-Shapiro:1991">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduces the measure LEVERAGE which is the simplest function which
	satisfies his principles for rule-interest functions (0 if the variables
	are statistically independent; monotonically increasing if the variables
	occur more often together; monotonically decreasing if one of the
	variables alone occurs more often).
</font></blockquote>
<blockquote><font size="-1">
Keywords: kdd, measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Smyth:1991">4</a>]
</td>
<td class="bibtexitem">
Padhraic Smyth and R.&nbsp;Goodman.
 Rule induction using information theory.
 In <em>Knowledge Discovery in Databases</em>, 1991.
[&nbsp;<a href="association_rules_bib.html#arules:Smyth:1991">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduces the J-Measure as a scaled measures of cross entropy for the information content of a rule.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Agrawal:1993b">5</a>]
</td>
<td class="bibtexitem">
Rakesh Agrawal, Tomasz Imielinski, and Arun Swami.
 Database mining: A performance perspective.
 <em>IEEE Transactions on Knowledge and Data Engineering</em>,
  5(6):914--925, 1993.
[&nbsp;<a href="association_rules_bib.html#arules:Agrawal:1993b">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1109/69.250074">DOI</a>&nbsp;]
<blockquote><font size="-1">
Places association rule mining together with classification and sequence
	mining into the context of rule discovery in database mining. The
	authors basic operations and an algorithm to discover classification
	rules. For the evaluation they generate artificial survey data using
	different classification functions.
</font></blockquote>
<blockquote><font size="-1">
Keywords: evaluation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Agrawal:1993">6</a>]
</td>
<td class="bibtexitem">
R.&nbsp;Agrawal, T.&nbsp;Imielinski, and A.&nbsp;Swami.
 Mining association rules between sets of items in large databases.
 In <em>Proceedings of the ACM SIGMOD International Conference on
  Management of Data</em>, pages 207--216, Washington D.C., May 1993.
[&nbsp;<a href="association_rules_bib.html#arules:Agrawal:1993">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/170036.170072">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces association rules and the SUPPORT-CONFIDENCE framework
	and an algorithm to mine large itemsets. The algorithm is sometimes
	called AIS after the authors initials.
</font></blockquote>
<blockquote><font size="-1">
Keywords: algorithm
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Klemettinen:1994">7</a>]
</td>
<td class="bibtexitem">
Mika Klemettinen, Heikki Mannila, Pirjo Ronkainen, Hannu Toivonen, and
  A.&nbsp;Inkeri Verkamo.
 Finding interesting rules from large sets of discovered association
  rules.
 In Nabil&nbsp;R. Adam, Bharat&nbsp;K. Bhargava, and Yelena Yesha, editors, <em>
  Third International Conference on Information and Knowledge Management
  (CIKM'94)</em>, pages 401--407. ACM Press, 1994.
[&nbsp;<a href="association_rules_bib.html#arules:Klemettinen:1994">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/191246.191314">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduce the usage of rule templates.
</font></blockquote>
<blockquote><font size="-1">
Keywords: constraint
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Mannila:1994">8</a>]
</td>
<td class="bibtexitem">
Heikki Mannila, Hannu Toivonen, and A.&nbsp;Inkeri Verkamo.
 Efficient algorithms for discovering association rules.
 In Usama&nbsp;M. Fayyad and Ramasamy Uthurusamy, editors, <em>AAAI
  Workshop on Knowledge Discovery in Databases (KDD-94)</em>, pages 181--192,
  Seattle, Washington, 1994. AAAI Press.
[&nbsp;<a href="association_rules_bib.html#arules:Mannila:1994">bib</a>&nbsp;]
<blockquote><font size="-1">
Develop similar improvements to the candidate generation as APRIORI.
	Itemsets with support are called covering sets. The paper also introduces
	sampling from the database and gives bounds for the resulting estimate
	of support.
</font></blockquote>
<blockquote><font size="-1">
Keywords: algorithm,sampling
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Agrawal:1994">9</a>]
</td>
<td class="bibtexitem">
Rakesh Agrawal and Ramakrishnan Srikant.
 Fast algorithms for mining association rules in large databases.
 In Jorge&nbsp;B. Bocca, Matthias Jarke, and Carlo Zaniolo, editors, <em>
  Proceedings of the 20th International Conference on Very Large Data Bases,
  VLDB</em>, pages 487--499, Santiago, Chile, September 1994.
[&nbsp;<a href="association_rules_bib.html#arules:Agrawal:1994">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduction of the APRIORI algorithm (the best-known algorithm; it
	uses a breadth-first search strategy to counting the support of itemsets).
	The algorithm uses an improved candidate generation function which
	exploits the downward closure property of support and makes it more
	efficient than AIS. Also an algorithm to generate synthetic transaction
	data is presented. Such synthetic transaction data are widely used
	for the evaluation and comparison of new algorithms.
</font></blockquote>
<blockquote><font size="-1">
Keywords: algorithm, evaluation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Agrawal:1995">10</a>]
</td>
<td class="bibtexitem">
Rakesh Agrawal and Ramakrishnan Srikant.
 Mining sequential patterns.
 In Philip&nbsp;S. Yu and Arbee S.&nbsp;P. Chen, editors, <em>Eleventh
  International Conference on Data Engineering</em>, pages 3--14, Taipei, Taiwan,
  1995. IEEE Computer Society Press.
[&nbsp;<a href="association_rules_bib.html#arules:Agrawal:1995">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduces mining sequential patterns. A sequential pattern is a maximal
	sequence that exceeds minimum support (a minimum number of customers).
	The algorithms AprioriSome and AprioryAll (based on Apriori) are
	presented.
</font></blockquote>
<blockquote><font size="-1">
Keywords: sequential
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Savasere:1995">11</a>]
</td>
<td class="bibtexitem">
Ashok Savasere, Edward Omiecinski, and Shamkant Navathe.
 An efficient algorithm for mining association rules in large
  databases.
 In <em>Proceedings of the 21st VLDB Conference</em>, pages 432--443,
  Zurich, Switzerland, 1995.
[&nbsp;<a href="association_rules_bib.html#arules:Savasere:1995">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduction of the PARTITION algorithm. The database is scanned only
	twice. For the first scan the DB is partitioned and in each partition
	support is counted. Then the counts are merged to generate potential
	large itemsets. In the second scan the potential large itemsets are
	counted to find the actual large itemsets.
</font></blockquote>
<blockquote><font size="-1">
Keywords: algorithm
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Srikant:1995">12</a>]
</td>
<td class="bibtexitem">
Ramakrishnan Srikant and Rakesh Agrawal.
 Mining generalized association rules.
 In <em>Proceedings of the 21st VLDB Conference, Zurich,
  Switzerland</em>, 1995.
[&nbsp;<a href="association_rules_bib.html#arules:Srikant:1995">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1016/s0167-739x(97)00019-8">DOI</a>&nbsp;]
<blockquote><font size="-1">
Generalized association rules use a taxonomy (is-a hierarchy) on items.
	The paper introduces R-interesting rules as rules with a support
	which is R-times higher than the support of its closest ancestor
	(a rule with at leased on item generalized). Algorithms that use
	R-interesting in addition to support and confidence are presented
	and evaluated.
</font></blockquote>
<blockquote><font size="-1">
Keywords: generalized
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Bernard:1996">13</a>]
</td>
<td class="bibtexitem">
Jean-Marc Bernard and Camilo Charron.
 L'analyse implicative bayésienne : une méthode pour l'étude des
  dépendances orientées. 2. modele logique sur un tableau de contingence.
 <em>Mathématiques et sciences humaines</em>, 134:5--18, 1996.
[&nbsp;<a href="association_rules_bib.html#arules:Bernard:1996">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.4000/msh.2734">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces Varying Rates Liaison.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Fayyad:1996">14</a>]
</td>
<td class="bibtexitem">
Usama&nbsp;M. Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth.
 <em>From Data Mining to Knowledge Discovery: An Overview</em>, pages
  1--36.
 MIT Press, Cambridge, MA, 1996.
[&nbsp;<a href="association_rules_bib.html#arules:Fayyad:1996">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduction to the KDD process.
</font></blockquote>
<blockquote><font size="-1">
Keywords: kdd
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Fukuda:1996">15</a>]
</td>
<td class="bibtexitem">
Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takeshi Tokuyama.
 Mining optimized association rules for numeric attributes.
 In <em>PODS '96 Proceedings of the fifteenth ACM
  SIGACT-SIGMOD-SIGART symposium on Principles of database systems</em>, pages
  182--191. ACM Press, 1996.
[&nbsp;<a href="association_rules_bib.html#arules:Fukuda:1996">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1006/jcss.1998.1595">DOI</a>&nbsp;]
<blockquote><font size="-1">
Finds appropriate
  ranges for quantitative attributes automatically
  by maximizing the support on the
  condition that the confidence ratio is at least a given
  threshold value or by maximizing the confidence
  ratio on the condition that the support is at least a given
  threshold number.
  The paper also introduces the measure gain: gain(R) = sup(R) - minConf * sup(lhs(R)) = sup(R) * (conf(R) - minConf).
</font></blockquote>
<blockquote><font size="-1">
Keywords: quantitative
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Gras:1996">16</a>]
</td>
<td class="bibtexitem">
Régis Gras, Saddo Ag&nbsp;Almouloud, Marc Bailleul, Annie Larher, Maria Polo,
  Harrisson Ratsimba-Rajohn, and André Totohasina.
 <em>L'implication statistique, nouvelle méthode exploratoire de
  données</em>.
 ARDM, 01 1996.
[&nbsp;<a href="association_rules_bib.html#arules:Gras:1996">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduces the implication index.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Mannila:1996">17</a>]
</td>
<td class="bibtexitem">
Heikki Mannila and Hannu Toivonen.
 Multiple uses of frequent sets and condensed representations.
 In <em>Proceedings of the Second International Conference on
  Knowledge Discovery and Data Mining (KDD-96)</em>, pages 189--194. AAAI Press,
  1996.
[&nbsp;<a href="association_rules_bib.html#arules:Mannila:1996">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduces general rules with disjunctions and negations in the antecedent
	and the consequent. The confidence of any such rules can be approximated
	by using the support of frequent itemsets only (applying the inclusion-exclusion
	principle). Using the negative border, an error bound for the estimates
	can be calculated. The authors also show that frequent itemsets with
	a support of epsilon are a concise representation (epsilon-adequate
	representation) which can approximate the confidence of any itemset
	with an error of at most epsilon.
</font></blockquote>
<blockquote><font size="-1">
Keywords: concise
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Toivonen:1996">18</a>]
</td>
<td class="bibtexitem">
Hannu Toivonen.
 Sampling large databases for association rules.
 In <em>VLDB '96: Proceedings of the 22th International Conference on
  Very Large Data Bases</em>, pages 134--145, San Francisco, CA, USA, 1996. Morgan
  Kaufmann Publishers Inc.
[&nbsp;<a href="association_rules_bib.html#arules:Toivonen:1996">bib</a>&nbsp;]
<blockquote><font size="-1">
Find frequent itemsets in a random sample of a database (that fits
	into main memory) and then verify the found frequent itemsets in
	the database.
</font></blockquote>
<blockquote><font size="-1">
Keywords: algorithm,sampling
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Lent:1996">19</a>]
</td>
<td class="bibtexitem">
Brian Lent, Arun&nbsp;N. Swami, and Jennifer Widom.
 Clustering association rules.
 In <em>Proceedings of the Thirteenth International Conference on
  Data Engineering, April 7--11, 1997 Birmingham U.K.</em>, pages 220--231. IEEE
  Computer Society, 1997.
[&nbsp;<a href="association_rules_bib.html#arules:Lent:1996">bib</a>&nbsp;]
<blockquote><font size="-1">
Join adjacent intervals for quantitative association rules to produce
	more general rules.
</font></blockquote>
<blockquote><font size="-1">
Keywords: clustering,quantitative
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Mannila:1997">20</a>]
</td>
<td class="bibtexitem">
Heikki Mannila, Hannu Toivonen, and A.&nbsp;Inkeri Verkamo.
 Discovery of frequent episodes in event sequences.
 <em>Data Mining and Knowledge Discovery</em>, 1(3):259--289, 1997.
[&nbsp;<a href="association_rules_bib.html#arules:Mannila:1997">bib</a>&nbsp;]
<blockquote><font size="-1">
Keywords: sequential
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Srikant:1997">21</a>]
</td>
<td class="bibtexitem">
Ramakrishnan Srikant, Quoc Vu, and Rakesh Agrawal.
 Mining association rules with item constraints.
 In David Heckerman, Heikki Mannila, Daryl Pregibon, and Ramasamy
  Uthurusamy, editors, <em>Proceedings of the 3rd International Conference
  Knowledge Discovery and Data Mining (KDD-97)</em>, pages 67--73. AAAI Press,
  1997.
[&nbsp;<a href="association_rules_bib.html#arules:Srikant:1997">bib</a>&nbsp;]
<blockquote><font size="-1">
Integrates BOOLEAN CONSTRAINTS on items (absence, presence) into the
	mining algorithm to reduce the search space. Algorithms are discussed.
</font></blockquote>
<blockquote><font size="-1">
Keywords: constraint
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Zaki:1997b">22</a>]
</td>
<td class="bibtexitem">
Mohammed&nbsp;Javeed Zaki, Srinivasan Parthasarathy, Wei Li, and Mitsunori Ogihara.
 Evaluation of sampling for data mining of association rules.
 In <em>Proceedings of the 7th International Workshop on Research
  Issues in Data Engineering (RIDE '97) High Performance Database Management
  for Large-Scale Applications</em>, pages 42--50. IEEE Computer Society, 1997.
[&nbsp;<a href="association_rules_bib.html#arules:Zaki:1997b">bib</a>&nbsp;]
<blockquote><font size="-1">
Evaluates random sampling with replacement as presented in Manila
	et al. 1994 using several datasets. The experiments show that Chernoff
	bounds overestimate the needed sample size and that sampling seems
	an effective tool for practical purposes.
</font></blockquote>
<blockquote><font size="-1">
Keywords: sampling
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Brin:1997">23</a>]
</td>
<td class="bibtexitem">
Sergey Brin, Rajeev Motwani, Jeffrey&nbsp;D. Ullman, and Shalom Tsur.
 Dynamic itemset counting and implication rules for market basket
  data.
 In <em>SIGMOD 1997, Proceedings ACM SIGMOD International Conference
  on Management of Data</em>, pages 255--264, Tucson, Arizona, USA, May 1997.
[&nbsp;<a href="association_rules_bib.html#arules:Brin:1997">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/253262.253325">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces CONVICTION (as an improvement to confidence based on implication
	rules) and INTEREST (later called LIFT).
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Brin:1997b">24</a>]
</td>
<td class="bibtexitem">
Sergey Brin, Rajeev Motwani, and Craig Silverstein.
 Beyond market baskets: Generalizing association rules to
  correlations.
 In <em>SIGMOD 1997, Proceedings ACM SIGMOD International Conference
  on Management of Data</em>, pages 265--276, Tucson, Arizona, USA, May 1997.
[&nbsp;<a href="association_rules_bib.html#arules:Brin:1997b">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/253262.253327">DOI</a>&nbsp;]
<blockquote><font size="-1">
Proposes to use the chi-square test for correlation. For an itemset
	of length l, the test is carried out on a l-dimensional contingency
	tables. A problem is cells with low counts and multiple tests.
</font></blockquote>
<blockquote><font size="-1">
Keywords: no-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Zaki:1997">25</a>]
</td>
<td class="bibtexitem">
Mohammed&nbsp;J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, and Wei Li.
 New algorithms for fast discovery of association rules.
 Technical Report 651, Computer Science Department, University of
  Rochester, Rochester, NY 14627, July 1997.
[&nbsp;<a href="association_rules_bib.html#arules:Zaki:1997">bib</a>&nbsp;]
<blockquote><font size="-1">
Quickly identify MAXIMAL FREQUENT ITEMSETS (a frequent itemset is
	maximal if it is no proper subset of any other frequent itemset)
	using different database layout schemes (regular, inverted) and clustering
	techniques (equivalence class ECLAT, max. clique). See also Zaki
	2000, Scalable Algorithms for Association Mining.
</font></blockquote>
<blockquote><font size="-1">
Keywords: maximal
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Aggarwal:1998">26</a>]
</td>
<td class="bibtexitem">
C.&nbsp;C. Aggarwal and P.&nbsp;S. Yu.
 A new framework for itemset generation.
 In <em>PODS 98, Symposium on Principles of Database Systems</em>, pages
  18--24, Seattle, WA, USA, 1998.
[&nbsp;<a href="association_rules_bib.html#arules:Aggarwal:1998">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/275487.275490">DOI</a>&nbsp;]
<blockquote><font size="-1">
Points out weaknesses of the large frequent itemset method using support
	(spuriousness, dense datasets) and that lift gives only values close
	to one for items which are very frequent, even if they are perfectly
	positive correlated. COLLECTIVE STRENGTH is introduced. Collective
	strength uses the violation rate for an itemset which is the fraction
	of transactions which contains some, but not all items of the itemset.
	The violation rate is compared to the expected violation rate under
	independence. Collective strength is downward closed.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Liu:1998">27</a>]
</td>
<td class="bibtexitem">
Bing Liu, Wynne Hsu, and Yiming Ma.
 Integrating classification and association rule mining.
 In <em>Proceedings of the 4rd International Conference Knowledge
  Discovery and Data Mining (KDD-98)</em>, pages 80--86. AAAI Press, 1998.
[&nbsp;<a href="association_rules_bib.html#arules:Liu:1998">bib</a>&nbsp;]
<blockquote><font size="-1">
Mines only the subset of association rules with the classification
	class attribute in the right-hand-site (CARs). From these CARs a
	classifier is built by using the rules with the highest confidence
	to cover the whole database. The presented alorithm is called Classification
	Based on Associations (CBA). In ecperiment the resulting classifiers
	are more accurate than C4.5.
</font></blockquote>
<blockquote><font size="-1">
Keywords: classification
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Megiddo:1998">28</a>]
</td>
<td class="bibtexitem">
Nimrod Megiddo and Ramakrishnan Srikant.
 Discovering predictive association rules.
 In Rakesh Agrawal, Paul&nbsp;E. Stolorz, and Gregory Piatetsky-Shapiro,
  editors, <em>Proceedings of the Fourth International Conference on Knowledge
  Discovery and Data Mining (KDD-98)</em>, pages 274--278. AAAI Press, 1998.
[&nbsp;<a href="association_rules_bib.html#arules:Megiddo:1998">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduces several STATISTICAL TESTS: Test if the observed support
	count is sig. greater than a support threshold, Chi-square test of
	independence (see also Brin et al. 1997). Also deals with the Bonferroni
	effect (multiple-comparison problem) by finding an upper bound of
	the number of tested hypotheses and proposing a resampling procedure
	using an independence model. The paper introduced confidence intervals
	for support and confidence. Finally, the authors find that the support-confidence
	framework does a good job to eliminate statistically insignificant
	rules (on market basket data).
</font></blockquote>
<blockquote><font size="-1">
Keywords: theory
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Ng:1998">29</a>]
</td>
<td class="bibtexitem">
Raymond&nbsp;T. Ng, Laks&nbsp;V.S. Lakshmanan, Jiawei Han, and Alex Pang.
 Exploratory mining and pruning optimizations of constrained
  associations rules.
 In <em>Proceedings of the ACM SIGMOD Conference, Seattle, WA</em>, pages
  13--24, 1998.
[&nbsp;<a href="association_rules_bib.html#arules:Ng:1998">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/276305.276307">DOI</a>&nbsp;]
<blockquote><font size="-1">
Characterizes various constraints (contains, minimum, maximum, count,
	sum, avg) according to anti-monotonicity and succinctness. Anti-monotonicity
	is the property which allows iterative pruning (generate and test
	candidates) used e.g., on support by Apriori. Succinctness is a property
	that enables us to generate only those itemsets which satisfy the
	constraint without the need to test them.
</font></blockquote>
<blockquote><font size="-1">
Keywords: constraint
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Silverstein:1998">30</a>]
</td>
<td class="bibtexitem">
Craig Silverstein, Sergey Brin, and Rajeev Motwani.
 Beyond market baskets: Generalizing association rules to dependence
  rules.
 <em>Data Mining and Knowledge Discovery</em>, 2:39--68, 1998.
[&nbsp;<a href="association_rules_bib.html#arules:Silverstein:1998">bib</a>&nbsp;]
<blockquote><font size="-1">
Journal version of Brin et al. (1997).
</font></blockquote>
<blockquote><font size="-1">
Keywords: no-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Zaki:1998">31</a>]
</td>
<td class="bibtexitem">
M.&nbsp;J. Zaki and M.&nbsp;Ogihara.
 Theoretical foundation of association rules.
 In <em>SIGMOD'98 Workshop on Research Issues in Data Mining and
  Knowledge Discovery (SIGMOD-DMKD'98), Seattle, Friday, June 5, 1998</em>, 1998.
[&nbsp;<a href="association_rules_bib.html#arules:Zaki:1998">bib</a>&nbsp;]
<blockquote><font size="-1">
Presents the lattice-theoretic foundations of mining associations
	based on FORMAL CONCEPT ANALYSIS and shows that frequent itemsets
	are determined by the set of frequent concepts. The paper studies
	the generation of a minimal set of rules (called base) can be generated
	from which all other association rules can be inferred. The paper
	also presents some complexity considerations using the connection
	between frequent itemsets and maximal bipartite cliques. It is shown
	that for very sparse databases association rule algorithms should
	scale linearly in the number of items.
</font></blockquote>
<blockquote><font size="-1">
Keywords: theory
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Bayardo:1999">32</a>]
</td>
<td class="bibtexitem">
Robert&nbsp;J. Bayardo Jr. and Rakesh Agrawal.
 Mining the most interesting rules.
 In <em>Proceedings of the fifth ACM SIGKDD international conference
  on Knowledge discovery and data mining (KDD-99)</em>, pages 145--154. ACM Press,
  1999.
[&nbsp;<a href="association_rules_bib.html#arules:Bayardo:1999">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/312129.312219">DOI</a>&nbsp;]
<blockquote><font size="-1">
Shows that for all rules with the same antecedent, the best (optimal,
	most interesting) rules according to measures as confidence, support,
	gain, chi-square value, gini, entropy gain, laplace, lift, conviction
	all must reside along a support/confidence border. The paper also
	shows that many measures are monotone functions of support and confidence.
</font></blockquote>
<blockquote><font size="-1">
Keywords: theory
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Li:1999">33</a>]
</td>
<td class="bibtexitem">
Jinyan Li, Xiuzhen Zhang, Guozho Dong, Kotagiri Ramamohanarao, and Qun Sun.
 Efficient mining of high confidence association rules without support
  thresholds.
 In J.&nbsp;Zytkow and J.&nbsp;Rauch, editors, <em>Principles of Data Mining
  and Knowledge Discovery PKDD'99, LNAI 1704, Prague, Czech Republic</em>, pages
  406--411. Springer-Verlag, 1999.
[&nbsp;<a href="association_rules_bib.html#arules:Li:1999">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/978-3-540-48247-5_50">DOI</a>&nbsp;]
<blockquote><font size="-1">
This paper used JUMPING EMERGING PATTERNS to mine a border for top
	rules (rules with 100% confidence) for a given consequent. The drawbacks
	are that only one consequent is mined at a time and that finding
	rules with other than 100% confidence is difficult.
</font></blockquote>
<blockquote><font size="-1">
Keywords: no-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Liu:1999">34</a>]
</td>
<td class="bibtexitem">
Bing Liu, Wynne Hsu, and Yiming Ma.
 Mining association rules with multiple minimum supports.
 In <em>Proceedings of the fifth ACM SIGKDD international conference
  on Knowledge discovery and data mining (KDD-99)</em>, pages 337--341. ACM Press,
  1999.
[&nbsp;<a href="association_rules_bib.html#arules:Liu:1999">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/312129.312274">DOI</a>&nbsp;]
<blockquote><font size="-1">
Adapts APRIORI to work with different minimum support thresholds assigned
	to different items (minimum item supports, MIS). To preserve the
	downward closure property of support item sorting using the MIS values
	is used.
</font></blockquote>
<blockquote><font size="-1">
Keywords: var-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Liu:1999b">35</a>]
</td>
<td class="bibtexitem">
Bing Liu, Wynne Hsu, and Yiming Ma.
 Pruning and summarizing the discovered associations.
 In <em>Proceedings of the fifth ACM SIGKDD international conference
  on Knowledge discovery and data mining (KDD-99)</em>, pages 125--134. ACM Press,
  1999.
[&nbsp;<a href="association_rules_bib.html#arules:Liu:1999b">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/312129.312216">DOI</a>&nbsp;]
<blockquote><font size="-1">
Remove insignificant rules using the chi-square test to test for correlation
	between the antecedent and the confident of a rule. Also DIRECTION
	SETTING (DS) RULES are introduced. A DS rule has a pos. correlated
	antecedent and consequent and is not built from a rule with a shorter
	antecedent which is a DS rule. Normally, only a small and concise
	fraction of rules are DS rules.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measures,theory
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Nada:1999">36</a>]
</td>
<td class="bibtexitem">
Nada Lavra&#x010D;, Peter Flach, and Blaz Zupan.
 Rule evaluation measures: A unifying view.
 In Sa&scaron;o D&#382;eroski and Peter Flach, editors, <em>Inductive
  Logic Programming</em>, pages 174--185, Berlin, Heidelberg, 1999. Springer Berlin
  Heidelberg.
[&nbsp;<a href="association_rules_bib.html#arules:Nada:1999">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/3-540-48751-4_17">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces relative accuracy/gain.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Pasquier:1999">37</a>]
</td>
<td class="bibtexitem">
Nicolas Pasquier, Yves Bastide, Rafik Taouil, and Lotfi Lakhal.
 Discovering frequent closed itemsets for association rules.
 In <em>Proceeding of the 7th International Conference on Database
  Theory, Lecture Notes In Computer Science (LNCS 1540)</em>, pages 398--416.
  Springer, 1999.
[&nbsp;<a href="association_rules_bib.html#arules:Pasquier:1999">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/3-540-49257-7_25">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces CLOSED ITEMSETS. An itemset X is closed if no proper super
	set of X is contained in every transaction in which X is contained.
	Which means there exists no super set of X with the same support
	count as X.
</font></blockquote>
<blockquote><font size="-1">
Keywords: closed
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Pasquier:1999b">38</a>]
</td>
<td class="bibtexitem">
Nicolas Pasquier, Yves Bastide, Rafik Taouil, and Lotfi Lakhal.
 Efficient mining of association rules using closed itemset lattices.
 <em>Information Systems</em>, 24(1):25--46, 1999.
[&nbsp;<a href="association_rules_bib.html#arules:Pasquier:1999b">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1016/s0306-4379(99)00003-4">DOI</a>&nbsp;]
<blockquote><font size="-1">
Present the CLOSE algorithm to mine frequent closed itemsets.
</font></blockquote>
<blockquote><font size="-1">
Keywords: closed
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:2000">39</a>]
</td>
<td class="bibtexitem">
Y.&nbsp;Bastide, R.&nbsp;Taouil, N.&nbsp;Pasquier, G.&nbsp;Stumme, and L.&nbsp;Lakhai.
 Mining frequent patterns with counting inference.
 <em>SIGKDD Explorations</em>, 2(2):66--75, 2000.
[&nbsp;<a href="association_rules_bib.html#arules:2000">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/380995.381017">DOI</a>&nbsp;]
<blockquote><font size="-1">
Proposes the algorithm PASCAL (a APRIORI optimization) to mine closed
	and frequent items. This approach uses frequent key-patterns to infer
	counts of frequent non-key patterns.
</font></blockquote>
<blockquote><font size="-1">
Keywords: closed
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Agarwal:2000">40</a>]
</td>
<td class="bibtexitem">
Rakesh&nbsp;C. Agrawal, Charu&nbsp;C. Aggarwal, and V.&nbsp;V.&nbsp;V. Prasad.
 Depth first generation of long patterns.
 In <em>Proceedings of the ACM SIGKDD International Conference on
  Knowledge Discovery and Data Mining (KDD-2000)</em>, pages 108--118, 2000.
[&nbsp;<a href="association_rules_bib.html#arules:Agarwal:2000">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/347090.347114">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces the algorithm DepthProject which builds a lexicographic
	tree in a depth first order.
</font></blockquote>
<blockquote><font size="-1">
Keywords: maximal
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Ahmed:2000">41</a>]
</td>
<td class="bibtexitem">
Khalil&nbsp;M. Ahmed, Nagwa&nbsp;M. El-Makky, and Yousry Taha.
 A note on &rdquo;Beyond market baskets: Generalizing association rules
  to correlations&rdquo;.
 <em>SIGKDD Explorations</em>, 1(2):46--48, 2000.
[&nbsp;<a href="association_rules_bib.html#arules:Ahmed:2000">bib</a>&nbsp;]
<blockquote><font size="-1">
A reply to Brin et al. (1997). The authors state that the chi-square
	test tests the whole contingency table, but for larger than 2x2 tables
	we want to test dependence for single cells.
</font></blockquote>
<blockquote><font size="-1">
Keywords: no-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Bayardo:2000">42</a>]
</td>
<td class="bibtexitem">
R.&nbsp;Bayardo, R.&nbsp;Agrawal, and D.&nbsp;Gunopulos.
 Constraint-based rule mining in large, dense databases.
 <em>Data Mining and Knowledge Discovery</em>, 4(2/3):217--240, 2000.
[&nbsp;<a href="association_rules_bib.html#arules:Bayardo:2000">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduces the MINIMUM IMPROVEMENT constraint for confidence (mine
	only rules with a confidence which is minimp greater than the confidence
	of any of its proper subset-rules). DenseMiner, an algorithm that
	enforces minimum support, minimum confidence and minimum improvement
	already during a breadth-first search for all rules for a given consequent
	C is presented.
</font></blockquote>
<blockquote><font size="-1">
Keywords: constraint
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Freitas:2000">43</a>]
</td>
<td class="bibtexitem">
Alex&nbsp;A. Freitas.
 Understanding the crucial differences between classification and
  discovery of association rules -- a position paper.
 <em>SIGKDD Explorations</em>, 2(1):65--69, 2000.
[&nbsp;<a href="association_rules_bib.html#arules:Freitas:2000">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/360402.360423">DOI</a>&nbsp;]
<blockquote><font size="-1">
Keywords: classification
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Hipp:2000">44</a>]
</td>
<td class="bibtexitem">
Jochen Hipp, Ulrich G&uuml;ntzer, and Gholamreza Nakhaeizadeh.
 Algorithms for association rule mining -- A general survey and
  comparison.
 <em>SIGKDD Explorations</em>, 2(2):1--58, 2000.
[&nbsp;<a href="association_rules_bib.html#arules:Hipp:2000">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/360402.360421">DOI</a>&nbsp;]
<blockquote><font size="-1">
Describes the fundamentals of association rule mining and presents
	an systematization of existing algorithms.
</font></blockquote>
<blockquote><font size="-1">
Keywords: algorithm
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:kddcup:2000">45</a>]
</td>
<td class="bibtexitem">
Ron Kohavi, Carla Brodley, Brian Frasca, Llew Mason, and Zijian Zheng.
 KDD-Cup 2000 organizers' report: Peeling the onion.
 <em>SIGKDD Explorations</em>, 2(2):86--98, 2000.
[&nbsp;<a href="association_rules_bib.html#arules:kddcup:2000">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/380995.381033">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces also some freely available data sets for algorithm performance
	evaluation.
</font></blockquote>
<blockquote><font size="-1">
Keywords: evaluation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Pei:2000">46</a>]
</td>
<td class="bibtexitem">
Jian Pei, Jiawei Han, and Runying Mao.
 CLOSET: An efficient algorithm for mining frequent closed
  itemsets.
 In <em>ACM SIGMOD Workshop on Research Issues in Data Mining and
  Knowledge Discovery</em>, 2000.
[&nbsp;<a href="association_rules_bib.html#arules:Pei:2000">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduces the algorithm CLOSET which mines frequent closed itemsets
	using FP-growth (a depth-first search using support counting).
</font></blockquote>
<blockquote><font size="-1">
Keywords: closed
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Silverstein:2000">47</a>]
</td>
<td class="bibtexitem">
Craig Silverstein, Sergey Brin, Rajeev Motwani, and Jeffrey&nbsp;D. Ullman.
 Scalable techniques for mining causal structures.
 <em>Data Mining and Knowledge Discovery</em>, 4(2/3):163--192, 2000.
[&nbsp;<a href="association_rules_bib.html#arules:Silverstein:2000">bib</a>&nbsp;]
<blockquote><font size="-1">
Explores the applicability of constraint-based causal discovery (known
	from Bayesian learning) to discover causal relationships in market
	basket data.
</font></blockquote>
<blockquote><font size="-1">
Keywords: causal
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Webb:2000">48</a>]
</td>
<td class="bibtexitem">
Geoffrey&nbsp;I. Webb.
 Efficient search for association rules.
 In <em>Proceedings of the 6th ACM SIGKDD International Conference on
  Knowledge Discovery and Data Mining, Boston, Massachusetts, United States,
  August 20 -- 23, 2000</em>, pages 99--107, 2000.
[&nbsp;<a href="association_rules_bib.html#arules:Webb:2000">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/347090.347112">DOI</a>&nbsp;]

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Wirth:2000">49</a>]
</td>
<td class="bibtexitem">
R&uuml;diger Wirth and Jochen Hipp.
 Crisp-dm: Towards a standard process model for data mining.
 In <em>Proceedings of the 4th International Conference on the
  Practical Applications of Knowledge Discovery and Data Mining</em>, Manchester,
  UK, April 2000.
[&nbsp;<a href="association_rules_bib.html#arules:Wirth:2000">bib</a>&nbsp;]
<blockquote><font size="-1">
Keywords: kdd
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Zaki:2000">50</a>]
</td>
<td class="bibtexitem">
Mohammed&nbsp;J. Zaki.
 Scalable algorithms for association mining.
 <em>IEEE Transactions on Knowledge and Data Engineering</em>,
  12(3):372--390, May/June 2000.
[&nbsp;<a href="association_rules_bib.html#arules:Zaki:2000">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1109/69.846291">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces six new algorithms combining several features (database
	format, the decomposition technique, and the search procedure). Includes
	Eclat (Equivalence CLAss Transformation), MaxEclat, Clique, MaxClique,
	TopDown, and AprClique. ECLAT is a well known depth-first search
	algorithm using set intersection.
</font></blockquote>
<blockquote><font size="-1">
Keywords: algorithm
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Adamo:2001">51</a>]
</td>
<td class="bibtexitem">
Jean-Marc Adamo.
 <em>Data Mining for Association Rules and Sequential Patterns</em>.
 Springer, New York, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Adamo:2001">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/978-1-4613-0085-4">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduction to association rules and mining sequential patterns.
</font></blockquote>
<blockquote><font size="-1">
Keywords: sequential
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Bay:2001">52</a>]
</td>
<td class="bibtexitem">
Stephen&nbsp;D. Bay and Michael&nbsp;J. Pazzani.
 Detecting group differences: Mining contrast sets.
 <em>Data Mining and Knowledge Discovery</em>, 5(3):213--246, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Bay:2001">bib</a>&nbsp;]
<blockquote><font size="-1">
Finds sets with substantially different support in different groups.
	Uses interest based pruning and statistical surprise for filtering
	(summarizing) contrast sets. The search error is controlled using
	different (Bonferroni) correction for sets of different size.
</font></blockquote>
<blockquote><font size="-1">
Keywords: theory
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Bruzzese:2001">53</a>]
</td>
<td class="bibtexitem">
Dario Bruzzese and Cristina Davino.
 Pruning of discovered association rules.
 <em>Computational Statistics</em>, 16:387--398, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Bruzzese:2001">bib</a>&nbsp;]
<blockquote><font size="-1">
The authors construct several statistical tests to evaluate the significance
	of discovered associations.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Burdick:2001">54</a>]
</td>
<td class="bibtexitem">
Douglas Burdick, Manuel Calimlim, and Johannes Gehrke.
 MAFIA: A maximal frequent itemset algorithm for
  transactional databases.
 In <em>Proceedings of the 17th International Conference on Data
  Engineering</em>, pages 443--452, Washington, DC, 2001. IEEE Computer Society.
[&nbsp;<a href="association_rules_bib.html#arules:Burdick:2001">bib</a>&nbsp;]
<blockquote><font size="-1">
MAFIA (MAximal Frequent Itemset Algorithm) finds maximal itemsets
	using a depth-first traversal of the itemset lattice, a compressed
	vertical bitmap representation of the database, additional pruning
	techniques (Parent Equivalence Pruning, Frequent Head Union Tail
	pruning) and dynamic reordering. The authors claim that MAFIA outperforms
	DepthProject (Agrawal et al., 2001) by a factor of 3 to 5 on average.
</font></blockquote>
<blockquote><font size="-1">
Keywords: maximal
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Cadez:2001">55</a>]
</td>
<td class="bibtexitem">
Igor&nbsp;V. Cadez, Padhraic Smyth, and Heikki Mannila.
 Probabilistic modeling of transaction data with applications to
  profiling, visualization, and prediction.
 In F.&nbsp;Provost and R.&nbsp;Srikant, editors, <em>Proceedings of the ACM
  SIGKDD Intentional Conference on Knowledge Discovery in Databases and Data
  Mining (KDD-01)</em>, pages 37--45. ACM Press, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Cadez:2001">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/502512.502523">DOI</a>&nbsp;]
<blockquote><font size="-1">
The authors construct a model (profile with weights) for each individual's
	behavior as a mixture of several components. This mixture provides
	the probabilities for a multinomial probability model (each item
	has a constant probability to be chosen for a transaction). Finally,
	the authors compare several estimation methods and model variants
	empirically using store choice data.
</font></blockquote>
<blockquote><font size="-1">
Keywords: evaluation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Cohen:2001">56</a>]
</td>
<td class="bibtexitem">
Edith Cohen, Mayur Datar, Shinji Fujiwara, Aristides Gionis, Piotr Indyk,
  Rajeev Motwani, Jeffrey&nbsp;D. Ullman, and Cheng Yang.
 Finding interesting associations without support pruning.
 <em>IEEE Transactions on Knowledge and Data Engineering</em>,
  13(1):64--78, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Cohen:2001">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1109/69.908981">DOI</a>&nbsp;]
<blockquote><font size="-1">
Uses similarity measures between hashed values of rows in a transaction
	database. The approach in the paper was only shown for associations
	between two items.
</font></blockquote>
<blockquote><font size="-1">
Keywords: no-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:DuMouchel:2001">57</a>]
</td>
<td class="bibtexitem">
William DuMouchel and Daryl Pregibon.
 Empirical Bayes screening for multi-item associations.
 In F.&nbsp;Provost and R.&nbsp;Srikant, editors, <em>Proceedings of the ACM
  SIGKDD Intentional Conference on Knowledge Discovery in Databases and Data
  Mining (KDD-01)</em>, pages 67--76. ACM Press, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:DuMouchel:2001">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/502512.502526">DOI</a>&nbsp;]
<blockquote><font size="-1">
Search for unusually frequent itemsets using statistical methods.
	First, the authors propose stratification of the data to avoid finding
	spurious associations within strata. Then the deviation of the observed
	frequency over a baseline frequency (based on independence) is used.
	Since the deviation is unreliable for low counts, an empirical Bayes
	model (its 95% confidence limit) is used to produce a posterior
	distribution of the true ratio of actual to baseline frequencies.
	The Bayes model gives ratios close to the observed ratios for large
	samples and reduces (shrinks) the ratio if the sample size gets small
	(to smooth away noise). For multi-item associations log-linear models
	are proposed to find higher order associations which cannot be explained
	by pairwise associations.
</font></blockquote>
<blockquote><font size="-1">
Keywords: no-support,theory
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Hofmann:2001">58</a>]
</td>
<td class="bibtexitem">
Heike Hofmann and Adalbert F.&nbsp;X. Wilhelm.
 Visual comparison of association rules.
 <em>Comput. Stat.</em>, 16(3):399--415, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Hofmann:2001">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/s001800100075">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces difference of confidence.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Kodratoff:2001">59</a>]
</td>
<td class="bibtexitem">
Yves Kodratoff.
 Comparing machine learning and knowledge discovery in databases: An
  application to knowledge discovery in texts.
 In Georgios Paliouras, Vangelis Karkaletsis, and Constantine&nbsp;D.
  Spyropoulos, editors, <em>Machine Learning and Its Applications, Advanced
  Lectures</em>, volume 2049 of <em>Lecture Notes in Computer Science</em>, pages
  1--21. Springer, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Kodratoff:2001">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/3-540-44673-7">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces casual support and casual confidence informed by negatives.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Pei:2001">60</a>]
</td>
<td class="bibtexitem">
Jian Pei, Jiawei Han, and Laks&nbsp;V.S. Lakshmanan.
 Mining frequent itemsets with convertible constraints.
 In <em>Proceedings of the 17th International Conference on Data
  Engineering, April 2--6, 2001, Heidelberg, Germany</em>, pages 433--442, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Pei:2001">bib</a>&nbsp;]
<blockquote><font size="-1">
Develops a technique of how constraints on avg, median and sum can
	be converted so that they can be used already during the search phase
	of the FP-growth algorithm. The constraints are classified into constraints
	that are: convertible anti-monotone, convertible monotone and strongly
	convertible.
</font></blockquote>
<blockquote><font size="-1">
Keywords: constraint
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Seno:2001">61</a>]
</td>
<td class="bibtexitem">
Masakazu Seno and George Karypis.
 Lpminer: An algorithm for finding frequent itemsets using length
  decreasing support constraint.
 In Nick Cercone, Tsau&nbsp;Young Lin, and Xindong Wu, editors, <em>
  Proceedings of the 2001 IEEE International Conference on Data Mining, 29
  November -- 2 December 2001, San Jose, California, USA</em>, pages 505--512. IEEE
  Computer Society, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Seno:2001">bib</a>&nbsp;]
<blockquote><font size="-1">
To find longer frequent itemsets, the minimal support requirement
	decreases as a function of the itemset length. A algorithm based
	on the FP-tree is presented and a property called small valid extension
	(SVE) is introduced which makes mining efficient in absence of downward
	closure.
</font></blockquote>
<blockquote><font size="-1">
Keywords: var-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Song:2001">62</a>]
</td>
<td class="bibtexitem">
Hee&nbsp;Seok Song, Soung&nbsp;Hie Kim, and Jae&nbsp;Kyeong Kim.
 A methodology for detecting the change of customer behavior based on
  association rule mining.
 In <em>Proceedings of the Pacific Asia Conference on Information
  System</em>, pages 871--885. PACIS, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Song:2001">bib</a>&nbsp;]
<blockquote><font size="-1">
Develops a methodology to detect changes of customer behavior automatically by comparing association rules between different time snapshots of data. Defines emerging pattern, unexpected change and the added/perished rule based on similarity and difference measures for rule matching.
</font></blockquote>
<blockquote><font size="-1">
Keywords: changing
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Wang:2001">63</a>]
</td>
<td class="bibtexitem">
Ke&nbsp;Wang, Yu&nbsp;He, and David&nbsp;W. Cheung.
 Mining confident rules without support requirement.
 In <em>Proceedings of the tenth international conference on
  Information and knowledge management</em>, pages 89 -- 96, New York, NY, 2001.
  ACM Press.
[&nbsp;<a href="association_rules_bib.html#arules:Wang:2001">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/502585.502601">DOI</a>&nbsp;]
<blockquote><font size="-1">
The paper shows that for data with categorical attributes a UNIVERSAL-EXISTENTIAL
	UPWARD CLOSURE exists for confidence. With this property algorithms
	with confidence-based pruning are possible that use a level-wise
	(from k to k-1) candidate generation are. The paper also discusses
	a disk-based implementation.
</font></blockquote>
<blockquote><font size="-1">
Keywords: no-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Zheng:2001">64</a>]
</td>
<td class="bibtexitem">
Zijian Zheng, Ron Kohavi, and Llew Mason.
 Real world performance of association rule algorithms.
 In F.&nbsp;Provost and R.&nbsp;Srikant, editors, <em>Proceedings of the ACM
  SIGKDD Intentional Conference on Knowledge Discovery in Databases and Data
  Mining (KDD-01)</em>, pages 401--406. ACM Press, 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Zheng:2001">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/502512.502572">DOI</a>&nbsp;]
<blockquote><font size="-1">
Compares the performance of association rule algorithms (APRIORI,
	CHARM, FP-growth, CLOSET, MagnumOpus) using one IBM-Artificial dataset
	and three real-world e-commerce datasets. It shows that some improvements
	demonstrated on artificial datasets do not carry over to real-world
	datasets.
</font></blockquote>
<blockquote><font size="-1">
Keywords: evaluation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Li:2001">65</a>]
</td>
<td class="bibtexitem">
Yingjiu Li, Peng Ning, X.&nbsp;Sean Wang, and Sushil Jajodia.
 Generating market basket data with temporal information.
 In <em>ACM KDD Workshop on Temporal Data Mining</em>, August 2001.
[&nbsp;<a href="association_rules_bib.html#arules:Li:2001">bib</a>&nbsp;]
<blockquote><font size="-1">
Develop a generator for synthetic data with temporal patterns based
	on the generator by Agrawal and Srikan (1994).
</font></blockquote>
<blockquote><font size="-1">
Keywords: sequential,evaluation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Aggarwal:2002">66</a>]
</td>
<td class="bibtexitem">
Charu&nbsp;C. Aggarwal, Cecilia&nbsp;Magdalena Procopiuc, and Philip&nbsp;S. Yu.
 Finding localized associations in market basket data.
 <em>Knowledge and Data Engineering</em>, 14(1):51--62, 2002.
[&nbsp;<a href="association_rules_bib.html#arules:Aggarwal:2002">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1109/69.979972">DOI</a>&nbsp;]
<blockquote><font size="-1">
Proposes to cluster transactions using a similarity measure based
	on the new affinity measure (measures similarity between pairs of
	items). Then mine association rules in the identified clusters.
</font></blockquote>
<blockquote><font size="-1">
Keywords: sampling
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Aze:2002">67</a>]
</td>
<td class="bibtexitem">
J.&nbsp;Azé and Y.&nbsp;Kodratoff.
 Evaluation de la résistance au bruit de quelques mesures
  d’extraction de règles d’assocation.
 In D.&nbsp;Hérin and D.A. Zighed, editors, <em>Extraction des
  connaissances et apprentissage</em>, volume&nbsp;1, pages 143--154. Hermes, 2002.
[&nbsp;<a href="association_rules_bib.html#arules:Aze:2002">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduces least contradiction.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Borgelt:2002">68</a>]
</td>
<td class="bibtexitem">
Christian Borgelt and Rudolf Kruse.
 Induction of association rules: Apriori implementation.
 In <em>15th Conference on Computational Statistics (Compstat 2002)</em>,
  Heidelberg, Germany, 2002. Physica Verlag.
[&nbsp;<a href="association_rules_bib.html#arules:Borgelt:2002">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/978-3-642-57489-4_59">DOI</a>&nbsp;]
<blockquote><font size="-1">
An efficient implementation of APRIORI.
</font></blockquote>
<blockquote><font size="-1">
Keywords: implementation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Calders:2002">69</a>]
</td>
<td class="bibtexitem">
Toon Calders and Bart Goethals.
 Mining all non-derivable frequent itemsets.
 In Tapio Elomaa, Heikki Mannila, and Hannu Toivonen, editors, <em>
  Proceedings of the 6th European Conference on Principles of Data Mining and
  Knowledge Discovery</em>, volume 2431 of <em>Lecture Notes in Computer Science</em>,
  pages 74--85. Springer-Verlag, 2002.
[&nbsp;<a href="association_rules_bib.html#arules:Calders:2002">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/3-540-45681-3_7">DOI</a>&nbsp;| 
<a href="http://arxiv.org/abs/cs/0206004">arXiv</a>&nbsp;]
<blockquote><font size="-1">
Introduce NON-DERIVABLE ITEMSETS (NDIs). The support of all frequent
	NDIs allows for computing the support of all frequent itemsets using
	deduction rules based on the inclusion-exclusion principle.
</font></blockquote>
<blockquote><font size="-1">
Keywords: concise
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Galiano:2002">70</a>]
</td>
<td class="bibtexitem">
F.&nbsp;Galiano, I.&nbsp;J. Blanco, D.&nbsp;S&aacute;nchez, and M.&nbsp;Vila.
 Measuring the accuracy and interest of association rules: A new
  framework.
 <em>Intell. Data Anal.</em>, 6:221--235, 2002.
[&nbsp;<a href="association_rules_bib.html#arules:Galiano:2002">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.3233/ida-2002-6303">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces casual support and casual confidence informed by negatives.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Stumme:2002">71</a>]
</td>
<td class="bibtexitem">
Gerd Stumme, Rafik Taouil, Yves Bastide, Nicolas Pasquier, and Lotfi Lakhal.
 Computing iceberg concept lattices with titanic.
 <em>Data &amp; Knowledge Engineering</em>, 42(2):189--222, 2002.
[&nbsp;<a href="association_rules_bib.html#arules:Stumme:2002">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1016/s0169-023x(02)00057-5">DOI</a>&nbsp;]
<blockquote><font size="-1">
The paper shows how iceberg concept lattices can be used as a condensed
	method to represent and visualize frequent (closed) itemsets. Iceberg
	concept lattices only show the top-most part of a concept lattices
	(known from Formal Concept Analysis). To compute iceberg concept
	lattices the algorithm TITANIC is presented which computes closed
	sets (a closure system) in a level-wise approach using weights (e.g.,
	support), equivalence classes and key sets (minimal sets in an equivalence
	class). TITANIC is compared experimentally to Next-Closure and performs
	better. PASCAL (Bastide et al. 2000) is a modified version of TITANIC
	to mine all frequent itemsets.
</font></blockquote>
<blockquote><font size="-1">
Keywords: theory
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Zaki:2002">72</a>]
</td>
<td class="bibtexitem">
Mohammed&nbsp;J. Zaki and Ching-Jiu Hsiao.
 CHARM: An efficient algorithm for closed itemset mining.
 In <em>Proceedings of the Second SIAM International Conference on
  Data Mining</em>, Arlington, VA, 2002. SIAM.
[&nbsp;<a href="association_rules_bib.html#arules:Zaki:2002">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1137/1.9781611972726.27">DOI</a>&nbsp;]
<blockquote><font size="-1">
The algorithm CHARM enumerates all frequent closed itemsets and uses
	a number of improvements: (a) It uses a IT-tree (itemset-tidset tree
	based on equivalence classes) to search simultaneously the itemset
	space and the transaction space. (b) It uses a fast hash-based elimination
	of non-closed itemsets. (c) It uses diffsets which represents the
	database in a compact way which should fit into main memory. (d)
	It uses efficient intersection operations. The performance testing
	shows that CHARM can provide significant improvement over algorithms
	as Apriori, Close, Pascal, Mafia, and Closet.
</font></blockquote>
<blockquote><font size="-1">
Keywords: closed
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Aumann:2003">73</a>]
</td>
<td class="bibtexitem">
Y.&nbsp;Aumann and Y.&nbsp;Lindell.
 Statistical theory for quantitative association rules.
 <em>Journal of Intelligent Information Systems</em>, 20(3):255--283,
  2003.
[&nbsp;<a href="association_rules_bib.html#arules:Aumann:2003">bib</a>&nbsp;]
<blockquote><font size="-1">
Defines QUANTITATIVE ASSOCIATION RULES using statistical measures
	(e.g., mean and variance) of continuous data. Also algorithms are
	discussed.
</font></blockquote>
<blockquote><font size="-1">
Keywords: quantitative
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Barber:2003">74</a>]
</td>
<td class="bibtexitem">
Brock Barber and Howard&nbsp;J. Hamilton.
 Extracting share frequent itemsets with infrequent subsets.
 <em>Data Mining and Knowledge Discovery</em>, 7:153--185, 2003.
[&nbsp;<a href="association_rules_bib.html#arules:Barber:2003">bib</a>&nbsp;]
<blockquote><font size="-1">
ITEMSET SHARE is the fraction of some measure (e.g., sales, profit)
	contributed by the items in the set. A itemset is share frequent
	if it exceeds a threshold. Share frequency is not downward closed!
	The article presents several algorithms and heuristics to mine share
	frequent itemsets.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Boulicaut:2003">75</a>]
</td>
<td class="bibtexitem">
Jean-Francois Boulicaut, Artur Bykowski, and Christophe Rigotti.
 Free-sets: A condensed representation of boolean data for the
  approximation of frequency queries.
 <em>Data Mining and Knowledge Discovery</em>, 7(1):5--22, 2003.
[&nbsp;<a href="association_rules_bib.html#arules:Boulicaut:2003">bib</a>&nbsp;]
<blockquote><font size="-1">
Presents a new epsilon-adequate representation for frequent itemsets
	called frequent FREE-SETS. An itemset is a free-set if it has no
	subset with (almost) the same support thus the items in the itemset
	cannot be used to form a (nearly) exact rule.
</font></blockquote>
<blockquote><font size="-1">
Keywords: concise
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Omiecinski:2003">76</a>]
</td>
<td class="bibtexitem">
Edward&nbsp;R. Omiecinski.
 Alternative interest measures for mining associations in databases.
 <em>IEEE Transactions on Knowledge and Data Engineering</em>,
  15(1):57--69, Jan/Feb 2003.
[&nbsp;<a href="association_rules_bib.html#arules:Omiecinski:2003">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1109/tkde.2003.1161582">DOI</a>&nbsp;]
<blockquote><font size="-1">
Omiecinski introduced several alternatives to support. The first measure,
	ANY-CONFIDENCE, is defined as the confidence of the rule with the
	largest confidence which can be generated from an itemset. The author
	states that although finding all itemsets with a set any-confidence
	would enable us to find all rules with a given minimum confidence,
	any-confidence cannot be used efficiently as a measure of interestingness
	since confidence is not downward closed. The second introduced measure
	is ALL-CONFIDENCE. This measure is defined as the smallest confidence
	of all rules which can be produced from an itemset, i.e., all rules
	produced form an itemset will have a confidence greater or equal
	to its all-confidence value. BOND, the last measure, is defined as
	the ratio of the number of transactions which contain all items of
	an itemset to the number of transactions which contain at least one
	of these items. Omiecinski showed that bond and all-confidence are
	downward closed and, therefore, can be used for efficient mining
	algorithms.
</font></blockquote>
<blockquote><font size="-1">
Keywords: no-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Pavlov:2003">77</a>]
</td>
<td class="bibtexitem">
Dmitry Pavlov, Heikki Mannila, and Padhraic Smyth.
 Beyond independence: Probabilistic models for query approximation on
  binary transaction data.
 <em>IEEE Transactions on Knowledge and Data Engineering</em>,
  15(6):1409--1421, 2003.
[&nbsp;<a href="association_rules_bib.html#arules:Pavlov:2003">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1109/tkde.2003.1245281">DOI</a>&nbsp;]
<blockquote><font size="-1">
Investigates the use of probabilistic models (independence model,
	pair-wise interactions stored in a Chow-Liu Tree, mixtures of independence
	models, itemset inclusion-exclusion model, and the maximum entropy
	method) for the problem of generating fast approx. answers to queries
	for large sparse binary data sets.
</font></blockquote>
<blockquote><font size="-1">
Keywords: theory
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Ramesh:2003">78</a>]
</td>
<td class="bibtexitem">
Ganesh Ramesh, William&nbsp;A. Maniatty, and Mohammed&nbsp;J. Zaki.
 Feasible itemset distributions in data mining: theory and
  application.
 In <em>Symposium on Principles of Database Systems, PODS 2003</em>, San
  Diego, CA, USA, 2003. ACM Press.
[&nbsp;<a href="association_rules_bib.html#arules:Ramesh:2003">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/773153.773181">DOI</a>&nbsp;]
<blockquote><font size="-1">
Studies the length distributions of frequent and frequent maximal
	itemsets (the number of frequent itemsets with the same length).
	The length distribution determines the algorithms performance and
	is important to generate realistic synthetic datasets.
</font></blockquote>
<blockquote><font size="-1">
Keywords: theory
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Sung:2003">79</a>]
</td>
<td class="bibtexitem">
Sam&nbsp;Y. Sung, Zhao Li, Chew&nbsp;L. Tan, and Peter&nbsp;A. Ng.
 Forecasting association rules using existing data sets.
 <em>IEEE Transactions on Knowledge and Data Engineering</em>,
  15(6):1448--1459, Nov/Dec 2003.
[&nbsp;<a href="association_rules_bib.html#arules:Sung:2003">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1109/tkde.2003.1245284">DOI</a>&nbsp;]
<blockquote><font size="-1">
Resample datasets proportional to background attributes (e.g., distribution
	of customers' sex) to forecast rules in a new situation (e.g., a
	new store at a new location).
</font></blockquote>
<blockquote><font size="-1">
Keywords: sampling
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Tao:2003">80</a>]
</td>
<td class="bibtexitem">
Feng Tao, Fionn Murtagh, and Mohsen Farid.
 Weighted association rule mining using weighted support and
  significance framework.
 In <em>Proceedings of The Ninth ACM SIGKDD International Conference
  on Knowledge Discovery and Data Mining (KDD-2003)</em>, Washington, DC, 2003. ACM
  Press.
[&nbsp;<a href="association_rules_bib.html#arules:Tao:2003">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/956750.956836">DOI</a>&nbsp;]
<blockquote><font size="-1">
Uses attributes of the items (e.g., price, page dwelling time) to
	WEIGHT SUPPORT. A support and significance framework is presented
	which possesses a weighted downward closure property important for
	pruning the search space.
</font></blockquote>
<blockquote><font size="-1">
Keywords: var-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Hollmen:2003">81</a>]
</td>
<td class="bibtexitem">
Jaakko Hollm&eacute;n, Jouni&nbsp;K. Sepp&auml;nen, and Heikki Mannila.
 Mixture models and frequent sets: Combining global and local methods
  for 0--1 data.
 In <em>SIAM International Conference on Data Mining (SDM'03)</em>, San
  Fransisco, May 2003.
[&nbsp;<a href="association_rules_bib.html#arules:Hollmen:2003">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1137/1.9781611972733.32">DOI</a>&nbsp;]
<blockquote><font size="-1">
Clusters binary data first using the EM-algorithms (looks like LCA;
	Cadez et al. (2001) seem to do the same to find profiles). Then the
	authors mine frequent itemsets in each cluster. Finally, they use
	the maximum entropy technique to obtain local models from the frequent
	itemsets and combine these models to approximate the joint distribution.
</font></blockquote>
<blockquote><font size="-1">
Keywords: clustering
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Borgelt:2003">82</a>]
</td>
<td class="bibtexitem">
Christian Borgelt.
 Efficient implementations of apriori and eclat.
 In Bart Goethals and Mohammed&nbsp;J. Zaki, editors, <em>Proceedings of
  the IEEE ICDM Workshop on Frequent Itemset Mining Implementations</em>,
  Melbourne, FL, USA, November 2003.
[&nbsp;<a href="association_rules_bib.html#arules:Borgelt:2003">bib</a>&nbsp;]
<blockquote><font size="-1">
Discusses the efficient implementation of APRIORI (with prefix tree)
	and ECLAT.
</font></blockquote>
<blockquote><font size="-1">
Keywords: implementation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Orlando:2003">83</a>]
</td>
<td class="bibtexitem">
Salvatore Orlando, Claudio Lucchese, Paolo Palmerini, Raffaele Perego, and
  Fabrizio Silvestri.
 kdci: a multi-strategy algorithm for mining frequent sets.
 In Bart Goethals and Mohammed&nbsp;J. Zaki, editors, <em>FIMI'03:
  Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining
  Implementations</em>, November 2003.
[&nbsp;<a href="association_rules_bib.html#arules:Orlando:2003">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduces the kDCI algorithm.
</font></blockquote>
<blockquote><font size="-1">
Keywords: algorithm,implementation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Xiong:2003">84</a>]
</td>
<td class="bibtexitem">
Hui Xiong, Pang-Ning Tan, and Vipin Kumar.
 Mining strong affinity association patterns in data sets with skewed
  support distribution.
 In Bart Goethals and Mohammed&nbsp;J. Zaki, editors, <em>Proceedings of
  the IEEE International Conference on Data Mining, November 19--22, 2003,
  Melbourne, Florida</em>, pages 387--394, November 2003.
[&nbsp;<a href="association_rules_bib.html#arules:Xiong:2003">bib</a>&nbsp;]
<blockquote><font size="-1">
Support-based pruning strategies are not effective for data sets with
	skewed support distributions. The authors propose the concept of
	hyperclique pattern, which uses an objective measure called h-confidence
	(equal to all-confidence by Omiecinski, 2003) to identify strong
	affinity patterns. The generation of so-called cross-support patterns
	(patterns with items with substantially different support) is avoided
	by h-confidence's cross-support property.
</font></blockquote>
<blockquote><font size="-1">
Keywords: no-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Coenen:2004">85</a>]
</td>
<td class="bibtexitem">
Frans Coenen, Graham Goulbourne, and Paul Leng.
 Tree structures for mining association rules.
 <em>Data Mining and Knowledge Discovery</em>, 8:25--51, 2004.
[&nbsp;<a href="association_rules_bib.html#arules:Coenen:2004">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1023/b:dami.0000005257.93780.3b">DOI</a>&nbsp;]
<blockquote><font size="-1">
Describes how to compute PARTIAL SUPPORT COUNTS in one DB-pass and
	how to store them in an enumeration tree (P-Tree).
</font></blockquote>
<blockquote><font size="-1">
Keywords: algorithm
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Coenen:2004a">86</a>]
</td>
<td class="bibtexitem">
Frans Coenen, Paul Leng, and Shakil Ahmed.
 Data structures for association rule mining: T-trees and P-trees.
 <em>IEEE Transactions on Knowledge and Data Engineering</em>,
  16(6):774--778, 2004.
[&nbsp;<a href="association_rules_bib.html#arules:Coenen:2004a">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1109/tkde.2004.8">DOI</a>&nbsp;]
<blockquote><font size="-1">
Describes two new structures for association rule mining: T-trees
	(total support trees) and P-trees (partial support trees). The T-tree
	is a data structure (a compressed set enumeration tree) to store
	itemsets. The P-tree is a compressed way to represent a database
	in memory for mining.
</font></blockquote>
<blockquote><font size="-1">
Keywords: implementation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Goethals:2004">87</a>]
</td>
<td class="bibtexitem">
Bart Goethals and Mohammed&nbsp;J. Zaki.
 Advances in frequent itemset mining implementations: Report on
  FIMI'03.
 <em>SIGKDD Explorations</em>, 6(1):109--117, 2004.
[&nbsp;<a href="association_rules_bib.html#arules:Goethals:2004">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/1007730.1007744">DOI</a>&nbsp;]
<blockquote><font size="-1">
This paper reports on the performance of different frequent itemset
	mining implementations on several real-world and artificial databases.
	The authors conclude that the latest algorithms (patricia, kdci,
	fpclose, fpmax*) outperform older ones but that currently no tested
	algorithm gracefully scales up to very large databases with millions
	of transactions.
</font></blockquote>
<blockquote><font size="-1">
Keywords: implementation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Han:2004">88</a>]
</td>
<td class="bibtexitem">
Jiawei Han, Jian Pei, Yiwen Yin, and Runying Mao.
 Mining frequent patterns without candidate generation.
 <em>Data Mining and Knowledge Discovery</em>, 8:53--87, 2004.
[&nbsp;<a href="association_rules_bib.html#arules:Han:2004">bib</a>&nbsp;]
<blockquote><font size="-1">
Describes the data mining method FP-growth (frequent pattern growth)
	which uses an extended prefix-tree (FP-tree) structure to store the
	database in a compressed form. FP-growth adopts a divide-and-conquer
	approach to decompose both the mining tasks and the databases. It
	uses a pattern fragment growth method to avoid the costly process
	of candidate generation and testing.
</font></blockquote>
<blockquote><font size="-1">
Keywords: algorithm
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Sistrom:2004">89</a>]
</td>
<td class="bibtexitem">
CL&nbsp;Sistrom and CW&nbsp;Garvan.
 Proportions, odds, and risk.
 <em>Radiology</em>, 230(1):12--19, 2004.
[&nbsp;<a href="association_rules_bib.html#arules:Sistrom:2004">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1148/radiol.2301031028">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces relative risk.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Tan:2004">90</a>]
</td>
<td class="bibtexitem">
Pang-Ning Tan, Vipin Kumar, and Jaideep Srivastava.
 Selecting the right objective measure for association analysis.
 <em>Information Systems</em>, 29(4):293--313, 2004.
[&nbsp;<a href="association_rules_bib.html#arules:Tan:2004">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1016/s0306-4379(03)00072-3">DOI</a>&nbsp;]
<blockquote><font size="-1">
Compare the properties of 21 objective measures (of interest). The
	measures in general lack to agree with each other. However, the authors
	show that if support-based pruning or table standardization (of the
	contingency tables) is used, the measures become highly correlated.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measures
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Yang:2004">91</a>]
</td>
<td class="bibtexitem">
Guizhen Yang.
 The complexity of mining maximal frequent itemsets and maximal
  frequent patterns.
 In <em>Proceedings of the 2004 ACM SIGKDD international conference
  on Knowledge discovery and data mining</em>, Seattle, WA, USA, 2004. ACM Press.
[&nbsp;<a href="association_rules_bib.html#arules:Yang:2004">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/1014052.1014091">DOI</a>&nbsp;]
<blockquote><font size="-1">
Shows that enumerating all maximal frequent itemsets is NP-hard and
	the associated counting problem is #P-complete.
</font></blockquote>
<blockquote><font size="-1">
Keywords: theory
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Zaki:2004">92</a>]
</td>
<td class="bibtexitem">
Mohammed Zaki.
 Mining non-redundant association rules.
 <em>Data Mining and Knowledge Discovery</em>, 9:223--248, 2004.
[&nbsp;<a href="association_rules_bib.html#arules:Zaki:2004">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1023/b:dami.0000040429.96086.c7">DOI</a>&nbsp;]
<blockquote><font size="-1">
Compares frequent itemsets and frequent closed itemsets and shows
	that frequent closed itemsets can be used to generate NON-REDUNDANT
	association rules. Non-Redundant rules are a set of rules with the
	most general rules (smallest antecedent and consequent) without loss
	of information.
</font></blockquote>
<blockquote><font size="-1">
Keywords: closed
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Blanchard:2005">93</a>]
</td>
<td class="bibtexitem">
Julien Blanchard, Fabrice Guillet, Henri Briand, and Regis Gras.
 Assessing rule interestingness with a probabilistic measure of
  deviation from equilibrium.
 In <em>Proceedings of the 11th international symposium on Applied
  Stochastic Models and Data Analysis ASMDA-2005</em>, pages 191--200. ENST, 2005.
[&nbsp;<a href="association_rules_bib.html#arules:Blanchard:2005">bib</a>&nbsp;]
<blockquote><font size="-1">
Presents a statistical test for the deviation from the equilibrium
	of a rule. The equilibrium for rule a -&gt; b is defined as: the number
	of transactions which contain a and b together is equal to the number
	of transactions which contain a and not b.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Bonchi:2005">94</a>]
</td>
<td class="bibtexitem">
Francesco Bonchi, Fosca Giannotti, Alessio Mazzanti, and Dino Pedreschi.
 ExAnte: A preprocessing method for frequent-pattern mining.
 <em>IEEE Intelligent Systems</em>, 20(3):25--31, 2005.
[&nbsp;<a href="association_rules_bib.html#arules:Bonchi:2005">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1109/mis.2005.45">DOI</a>&nbsp;]
<blockquote><font size="-1">
Reduces the database size before mining by iteratively applying mu-reduction
	and alpha-reduction. Mu-reduction removes transactions which do not
	meet monotone constraints. Alpha-reduction remove infrequent items
	from the transactions.
</font></blockquote>
<blockquote><font size="-1">
Keywords: constraint
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Gouda:2005">95</a>]
</td>
<td class="bibtexitem">
Karam Gouda and Mohammed&nbsp;J. Zaki.
 GenMax: An efficient algorithm for mining maximal frequent
  itemsets.
 <em>Data Mining and Knowledge Discovery</em>, 11:1--20, 2005.
[&nbsp;<a href="association_rules_bib.html#arules:Gouda:2005">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/s10618-005-0002-x">DOI</a>&nbsp;]
<blockquote><font size="-1">
Presents a backtrack search based algorithm for mining maximal frequent
	itemsets. Uses: progressive focusing for maximality checking, and
	diffset propagation for frequency computation.
</font></blockquote>
<blockquote><font size="-1">
Keywords: maximal
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Jeske:2005">96</a>]
</td>
<td class="bibtexitem">
Daniel&nbsp;R. Jeske, Behrokh Samadi, Pengyue&nbsp;J. Lin, Lan Ye, Sean Cox, Rui Xiao,
  Ted Younglove, Minh Ly, Douglas Holt, and Ryan Rich.
 Generation of synthetic data sets for evaluating the accuracy of
  knowledge discovery systems.
 In <em>Proceeding of the eleventh ACM SIGKDD international
  conference on Knowledge discovery in data mining</em>, pages 756--762, New York,
  NY, USA, 2005. ACM Press.
[&nbsp;<a href="association_rules_bib.html#arules:Jeske:2005">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/1081870.1081969">DOI</a>&nbsp;]
<blockquote><font size="-1">
Generate synthetic data (e.g., credit card transaction data) for accuracy
	evaluation using semantic graphs.
</font></blockquote>
<blockquote><font size="-1">
Keywords: evaluation
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Scheffer:2005">97</a>]
</td>
<td class="bibtexitem">
Tobias Scheffer.
 Finding association rules that trade support optimally against
  confidence.
 <em>Intelligent Data Analysis</em>, 9(4):381--395, 2005.
[&nbsp;<a href="association_rules_bib.html#arules:Scheffer:2005">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.3233/ida-2005-9405">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces predictive accuracy which is the expected value of the
	confidence of a rules with respect to the process underlying the
	database. The author shows how predictive accuracy can be calculated
	from confidence and support measured on a data set using a Bayesian
	frequency correction (very simplified: confidence is discounted for
	rules with low supports). Also an algorithm is presented which finds
	the top n most predictive association rules (redundant rules with
	a 0 predictive accuracy improvement are removed) and shows how to
	estimate the prior distribution needed for the correction.
</font></blockquote>
<blockquote><font size="-1">
Keywords: theory,measures
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Seno:2005">98</a>]
</td>
<td class="bibtexitem">
Masakazu Seno and George Karypis.
 Finding frequent itemsets using length-decreasing support constraint.
 <em>Data Mining and Knowledge Discovery</em>, 10:197--228, 2005.
[&nbsp;<a href="association_rules_bib.html#arules:Seno:2005">bib</a>&nbsp;]
<blockquote><font size="-1">
See Seno and Karypis 2001.
</font></blockquote>
<blockquote><font size="-1">
Keywords: var-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Webb:2003">99</a>]
</td>
<td class="bibtexitem">
Geoffrey&nbsp;I. Webb and Songmao&nbsp;S. Zhang.
 k-optimal-rule-discovery.
 <em>Data Mining and Knowledge Discovery</em>, 10(1):39--79, 2005.
[&nbsp;<a href="association_rules_bib.html#arules:Webb:2003">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/s10618-005-0255-4">DOI</a>&nbsp;]
<blockquote><font size="-1">
Develops GRD (based on the OPUS search strategy) which discovers all
	rules satisfying a set of constraints (max. number of rules, min
	support, min confidence, max coverage, max leverage) in a depth-first
	search. (An early draft of the paper was called: Beyond association
	rules: Generalized rule discovery)
</font></blockquote>
<blockquote><font size="-1">
Keywords: constraint
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Zaki:2005">100</a>]
</td>
<td class="bibtexitem">
Mohammed Zaki and Ching-Jui Hsiao.
 Efficient algorithms for mining closed itemsets and their lattice
  structure.
 <em>IEEE Transactions on Knowledge and Data Engineering</em>,
  17(4):462--478, 2005.
[&nbsp;<a href="association_rules_bib.html#arules:Zaki:2005">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1109/tkde.2005.60">DOI</a>&nbsp;]
<blockquote><font size="-1">
Describes the algorithm CHARM.
</font></blockquote>
<blockquote><font size="-1">
Keywords: closed
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Bonchi:2006">101</a>]
</td>
<td class="bibtexitem">
Francesco Bonchi and Claudio Lucchese.
 On condensed representations of constrained frequent patterns.
 <em>Knowledge and Information Systems</em>, 9(2):180--201, 2006.
[&nbsp;<a href="association_rules_bib.html#arules:Bonchi:2006">bib</a>&nbsp;]
<blockquote><font size="-1">
Presents an algorithm to efficiently mine closed and constrained frequent
	itemsets.
</font></blockquote>
<blockquote><font size="-1">
Keywords: closed,constraint
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Geng:2006">102</a>]
</td>
<td class="bibtexitem">
Liqiang Geng and Howard&nbsp;J. Hamilton.
 Interestingness measures for data mining: A survey.
 <em>ACM Computing Surveys</em>, 38(3):9, 2006.
[&nbsp;<a href="association_rules_bib.html#arules:Geng:2006">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/1132960.1132963">DOI</a>&nbsp;]
<blockquote><font size="-1">
Keywords: measures
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Li:2006">103</a>]
</td>
<td class="bibtexitem">
Jiuyong Li.
 On optimal rule discovery.
 <em>IEEE Transactions on Knowledge and Data Engineering</em>,
  18(4):460--471, 2006.
[&nbsp;<a href="association_rules_bib.html#arules:Li:2006">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1109/tkde.2006.1599385">DOI</a>&nbsp;]
<blockquote><font size="-1">
An optimal rule set (with respect to a metric of interestingness)
	contains all rules except those with no greater interestingness than
	one of its more general rules. An optimal rule set is a subset of
	a nonredundant rule set. The autors present an algorithm called ORD
	to find an optimal rule set. Classifiers build on optimal class association
	rules are at least as accurate as those built from CBA and C4.5 rule.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measures,classification
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Webb:2006">104</a>]
</td>
<td class="bibtexitem">
Geoffrey&nbsp;I. Webb.
 Discovering significant rules.
 In <em>KDD '06: Proceedings of the 12th ACM SIGKDD international
  conference on Knowledge discovery and data mining</em>, pages 434--443, New York,
  NY, USA, 2006. ACM Press.
[&nbsp;<a href="association_rules_bib.html#arules:Webb:2006">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/1150402.1150451">DOI</a>&nbsp;]
<blockquote><font size="-1">
Comapares two approaches (the well-known Bonferroni adjustment and
	a new evaluation using holdout data) to control the experimentwise
	risk of false discoveries for statistical hypothesis tests. Experimental
	results indicate that neither of the two approaches dominates the
	other.
</font></blockquote>
<blockquote><font size="-1">
Keywords: theory
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Calders:2006">105</a>]
</td>
<td class="bibtexitem">
Toon Calders, Christophe Rigotti, and Jean-Francois Boulicaut.
 A survey on condensed representations for frequent sets.
 In Jean-Francois Boulicaut, Luc Raedt, and Heikki Mannila, editors,
  <em>Constraint-Based Mining and Inductive Databases: European Workshop on
  Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March
  11-13, 2004, Revised Selected Papers</em>, volume 3848 of <em>Lecture Notes in
  Computer Science</em>, pages 64--80, February 2006.
[&nbsp;<a href="association_rules_bib.html#arules:Calders:2006">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/11615576_4">DOI</a>&nbsp;]
<blockquote><font size="-1">
Keywords: concise
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Hahsler:2006">106</a>]
</td>
<td class="bibtexitem">
Michael Hahsler.
 A model-based frequency constraint for mining associations from
  transaction data.
 <em>Data Mining and Knowledge Discovery</em>, 13(2):137--166, September
  2006.
[&nbsp;<a href="association_rules_bib.html#arules:Hahsler:2006">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/s10618-005-0026-2">DOI</a>&nbsp;]
<blockquote><font size="-1">
Develops a novel model-based frequency constraint as an alternative
	to a single, user-specified minimum support. The constraint utilizes
	knowledge of the process generating transaction data by applying
	a simple stochastic mixture model (the NB model) and uses a user-specified
	precision threshold to find local frequency thresholds for groups
	of itemsets (NB-frequent itemsets). The new constraint provides improvements
	over a single minimum support threshold and that the precision threshold
	is more robust and easier to set and interpret by the user.
</font></blockquote>
<blockquote><font size="-1">
Keywords: no-support
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Diatta:2007">107</a>]
</td>
<td class="bibtexitem">
Jean Diatta, Henri Ralambondrainy, and Andr&eacute; Totohasina.
 Towards a unifying probabilistic implicative normalized quality
  measure for association rules.
 In Fabrice&nbsp;J. Guillet and Howard&nbsp;J. Hamilton, editors, <em>Quality
  Measures in Data Mining</em>, pages 237--250. Springer Berlin Heidelberg, Berlin,
  Heidelberg, 2007.
[&nbsp;<a href="association_rules_bib.html#arules:Diatta:2007">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/978-3-540-44918-8_10">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces the Ralambondrainy measure.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Hahsler:2007">108</a>]
</td>
<td class="bibtexitem">
Michael Hahsler and Kurt Hornik.
 New probabilistic interest measures for association rules.
 <em>Intelligent Data Analysis</em>, 11(5):437--455, 2007.
[&nbsp;<a href="association_rules_bib.html#arules:Hahsler:2007">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.3233/IDA-2007-11502">DOI</a>&nbsp;| 
<a href="http://arxiv.org/abs/0803.0966">arXiv</a>&nbsp;| 
<a href="https://arxiv.org/abs/0803.0966">http</a>&nbsp;| 
<a href="http://michael.hahsler.net/research/hyperConfidence_IDA2007/hyperConfidence.pdf">.pdf</a>&nbsp;]
<blockquote><font size="-1">
Develops the interest
	measures, hyper-lift and hyper-confidence, which can be used to filter
	or order mined association rules. The measures are related to
	Fisher's exact test and 
	show significantly
	better performance than lift for applications where spurious rules
	are problematic.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measures
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Han:2007">109</a>]
</td>
<td class="bibtexitem">
J.&nbsp;Han, H.&nbsp;Cheng, D.&nbsp;Xin, and X.&nbsp;Yan.
 Frequent pattern mining: Current status and future directions.
 <em>Data Mining and Knowledge Discovery</em>, 14(1), 2007.
[&nbsp;<a href="association_rules_bib.html#arules:Han:2007">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/s10618-006-0059-1">DOI</a>&nbsp;]
<blockquote><font size="-1">
Complete overview of the state-of-the art in frequent patten mining and identifies future research directions.
</font></blockquote>
<blockquote><font size="-1">
Keywords: algorithm, concise, sequential
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Lenca:2007">110</a>]
</td>
<td class="bibtexitem">
Philippe Lenca, Beno&icirc;t Vaillant, Patrick Meyer, and Stephane Lallich.
 Association rule interestingness measures: Experimental and
  theoretical studies.
 In Fabrice&nbsp;J. Guillet and Howard&nbsp;J. Hamilton, editors, <em>Quality
  Measures in Data Mining</em>, pages 51--76. Springer Berlin Heidelberg, Berlin,
  Heidelberg, 2007.
[&nbsp;<a href="association_rules_bib.html#arules:Lenca:2007">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/978-3-540-44918-8_3">DOI</a>&nbsp;]
<blockquote><font size="-1">
Compares interest measures.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Kennett:2008">111</a>]
</td>
<td class="bibtexitem">
Ron Kenett and Silvia Salini.
 Relative linkage disequilibrium: A new measure for association rules.
 In Petra Perner, editor, <em>Advances in Data Mining. Medical
  Applications, E-Commerce, Marketing, and Theoretical Aspects</em>, pages
  189--199, Berlin, Heidelberg, 2008. Springer Berlin Heidelberg.
[&nbsp;<a href="association_rules_bib.html#arules:Kennett:2008">bib</a>&nbsp;]
<blockquote><font size="-1">
Introduces Relative Linkage Disequilibrium. 
    RLD is an association measure motivated by
    indices used in population genetics.
    It evaluates the deviation
    of the support of the whole rule from the support expected under
    independence given the supports of X and Y.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:McNicholas:2008">112</a>]
</td>
<td class="bibtexitem">
P.D. McNicholas, T.B. Murphy, and M.&nbsp;O’Regan.
 Standardising the lift of an association rule.
 <em>Computational Statistics &amp; Data Analysis</em>, 52(10):4712--4721,
  2008.
[&nbsp;<a href="association_rules_bib.html#arules:McNicholas:2008">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1016/j.csda.2008.03.013">DOI</a>&nbsp;]
<blockquote><font size="-1">
Standardized lift uses the minimum and maximum lift that can reach for each rule to standardize lift between 0 and 1.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Jalali-Heravi:2010">113</a>]
</td>
<td class="bibtexitem">
Mojdeh Jalali-Heravi and Osmar&nbsp;R. Za&iuml;ane.
 A study on interestingness measures for associative classifiers.
 In <em>Proceedings of the 2010 ACM Symposium on Applied Computing</em>,
  SAC '10, pages 1039--1046. ACM, 2010.
[&nbsp;<a href="association_rules_bib.html#arules:Jalali-Heravi:2010">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/1774088.1774306">DOI</a>&nbsp;]
<blockquote><font size="-1">
Compares associative classifiers using 53 different objective
  measures for association rules.
</font></blockquote>
<blockquote><font size="-1">
Keywords: classification
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Wu:2010">114</a>]
</td>
<td class="bibtexitem">
Tianyi Wu, Yuguo Chen, and Jiawei Han.
 Re-examination of interestingness measures in pattern mining: a
  unified framework.
 <em>Data Mining and Knowledge Discovery</em>, January 2010.
[&nbsp;<a href="association_rules_bib.html#arules:Wu:2010">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1007/s10618-009-0161-2">DOI</a>&nbsp;]
<blockquote><font size="-1">
Re-examines a set of null-invariant interestingness measures 
      (AllConf, Coherence, Cosine, Kulc, MaxConf) 
      and shows that they can be expressed as the generalized mathematical 
      mean, leading to a total ordering of them. 
      Also proposes a new measure called Imbalance Ratio.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Balcazar:2013">115</a>]
</td>
<td class="bibtexitem">
Jos&eacute;&nbsp;L. Balc&aacute;zar.
 Formal and computational properties of the confidence boost of
  association rules.
 <em>ACM Trans. Knowl. Discov. Data</em>, 7(4), December 2013.
[&nbsp;<a href="association_rules_bib.html#arules:Balcazar:2013">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1145/2541268.2541272">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces the measure confidence boost to help to obtain small and crisp sets of mined association rules.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Li:2014">116</a>]
</td>
<td class="bibtexitem">
Jiuyong Li, Jixue Liu, Hannu Toivonen, Kenji Satou, Youqiang Sun, and Bingyu
  Sun.
 Discovering statistically non-redundant subgroups.
 <em>Knowledge Based Systems</em>, 67:315–--327, 2014.
[&nbsp;<a href="association_rules_bib.html#arules:Li:2014">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1016/j.knosys.2014.04.030">DOI</a>&nbsp;]
<blockquote><font size="-1">
Uses a confidence interval around the rule's odds ratio
to define redundant rules. Following this definition, the paper presents an efficient alforithm to mine non-redundant rules.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Lopez:2014">117</a>]
</td>
<td class="bibtexitem">
Griselda López, Joaquín Abellán, Alfonso Montella, and Juan de&nbsp;Oña.
 Patterns of single-vehicle crashes on two-lane rural highways in
  granada province, spain: In-depth analysis through decision rules.
 <em>Transportation Research Record</em>, 2432(1):133--141, 2014.
[&nbsp;<a href="association_rules_bib.html#arules:Lopez:2014">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.3141/2432-16">DOI</a>&nbsp;]
<blockquote><font size="-1">
Introduces lift increase (LIC).
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Ochin:2008">118</a>]
</td>
<td class="bibtexitem">
Suresh Ochin and Nisheeth&nbsp;Joshi Kumar.
 Rule power factor: A new interest measure in associative
  classification.
 In <em>6th International Conference On Advances In Computing and
  Communications, ICACC 2016</em>, Cochin, India, 2016.
[&nbsp;<a href="association_rules_bib.html#arules:Ochin:2008">bib</a>&nbsp;| 
<a href="http://dx.doi.org/10.1016/j.procs.2016.07.175">DOI</a>&nbsp;]
<blockquote><font size="-1">
The rule power factor weights the confidence of a rule by its
            support.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure
</font></blockquote>

</td>
</tr>


<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="arules:Hahsler:2023">119</a>]
</td>
<td class="bibtexitem">
Michael Hahsler, Christian Buchta, Bettina Gruen, and Kurt Hornik.
 <em>arules: Mining Association Rules and Frequent Itemsets</em>, 2023.
 R package version 1.7-7.
[&nbsp;<a href="association_rules_bib.html#arules:Hahsler:2023">bib</a>&nbsp;| 
<a href="https://github.com/mhahsler/arules">http</a>&nbsp;]
<blockquote><font size="-1">
R infrastructure for association rule mining. Implements several algorithms and generalizations of measures.
</font></blockquote>
<blockquote><font size="-1">
Keywords: measure, algorithm
</font></blockquote>

</td>
</tr>
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