Quantitative CBA is a postprocessing algorithm for association rule classification algorithm CBA (, 1998). QCBA uses original, undiscretized numerical attributes to optimize the discovered association rules, refining the boundaries of literals in the antecedent of the rules produced by CBA. Some rules as well as literals from the rules can consequently be removed, which makes the resulting classifier smaller. One-rule classification and crisp rules make CBA classification models possibly most comprehensible among all association rule classification algorithms. These viable properties are retained by QCBA. The postprocessing is conceptually fast, because it is performed on a relatively small number of rules that passed data coverage pruning in CBA. Benchmark of our QCBA approach on 22 UCI datasets shows average 53 decrease in the total size of the model as measured by the total number of conditions in all rules. Model accuracy remains on the same level as for CBA.
@cite_15: We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
@cite_21: This paper is a survey of inductive rule learning algorithms that use a separate-and-conquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of algorithms into a single framework and analyze them along three different dimensions, namely their search, language and overfitting avoidance biases.
@cite_10: This paper introduces a novel fuzzy rule-based classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the well-known RIPPER algorithm, a state-of-the-art rule learner, while preserving its advantages, such as simple and comprehensible rule sets. In addition, it includes a number of modifications and extensions. In particular, FURIA learns fuzzy rules instead of conventional rules and unordered rule sets instead of rule lists. Moreover, to deal with uncovered examples, it makes use of an efficient rule stretching method. Experimental results show that FURIA significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy.
@cite_23: 
Separate-and-conquer strategy @cite_23 is likely the most commonly approach to rule learning. It provides a basis for the seminal RIPPER algorithm @cite_21 as well as for the state-of-the-art FURIA algorithm @cite_10 . Association rule learning is algorithmically different approach, which was originally designed to discover interesting patterns in very large and sparse instance spaces @cite_15 . It yields a set of conjunctive rules that correspond to high density regions in the data. Unlike in the typical separate-and-conquer approach, cardinal features need to be discretized prior to the execution of association rule learning and converted along with all nominal attributes to binary-valued features. The resulting rules correspond to hypercubes with boundaries aligned to the discretization breakpoints. This impaired precision is offset by computationally efficiency on high-dimensional data, allowing association rule learning to succeed where other approaches fail [p. 505] friedman2009elements .