A survey on condensed representations for frequent sets

T. Calders, C. Rigotti, J.F. Boulicaut

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    95 Citations (Scopus)


    Solving inductive queries which have to return complete collections of patterns satisfying a given predicate has been studied extensively the last few years. The specific problem of frequent set mining from potentially huge boolean matrices has given rise to tens of efficient solvers. Frequent sets are indeed useful for many data mining tasks, including the popular association rule mining task but also feature construction, association-based classification, clustering, etc. The research in this area has been boosted by the fascinating concept of condensed representations w.r.t. frequency queries. Such representations can be used to support the discovery of every frequent set and its support without looking back at the data. Interestingly, the size of condensed representations can be several orders of magnitude smaller than the size of frequent set collections. Most of the proposals concern exact representations while it is also possible to consider approximated ones, i.e., to trade computational complexity with a bounded approximation on the computed support values. This paper surveys the core concepts used in the recent works on condensed representation for frequent sets.
    Original languageEnglish
    Title of host publicationConstraint-Based Mining and Inductive Databases
    Subtitle of host publicationEuropean Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers
    EditorsJ.F. Boulicaut, L. De Raedt, H. Mannila
    Place of PublicationBerlin
    Number of pages17
    ISBN (Electronic)978-3-540-31351-9
    ISBN (Print)3-540-31331-1, 978-3-540-31331-1
    Publication statusPublished - 2005

    Publication series

    NameLecture Notes in Computer Science (LNCS)
    ISSN (Print)0302-9743


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