A Model for Managing Collections of Patterns
  Data mining algorithms are now able to efficiently deal with huge amount of
data. Various kinds of patterns may be discovered and may have some great
impact on the general development of knowledge. In many domains, end users may
want to have their data mined by data mining tools in order to extract patterns
that could impact their business. Nevertheless, those users are often
overwhelmed by the large quantity of patterns extracted in such a situation.
Moreover, some privacy issues, or some commercial one may lead the users not to
be able to mine the data by themselves. Thus, the users may not have the
possibility to perform many experiments integrating various constraints in
order to focus on specific patterns they would like to extract. Post processing
of patterns may be an answer to that drawback. Thus, in this paper we present a
framework that could allow end users to manage collections of patterns. We
propose to use an efficient data structure on which some algebraic operators
may be used in order to retrieve or access patterns in pattern bases.
