Mining all non-derivable frequent itemsets

T. Calders, B. Goethals

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

    168 Citations (Scopus)


    Recent studies on frequent itemset mining algorithms resulted in significant performance improvements. However, if the minimal support threshold is set too low, or the data is highly correlated, the number of frequent itemsets itself can be prohibitively large. To overcome this problem, recently several proposals have been made to construct a concise representation of the frequent itemsets, instead of mining all frequent itemsets. The main goal of this paper is to identify redundancies in the set of all frequent itemsets and to exploit these redundancies in order to reduce the result of a mining operation. We present deduction rules to derive tight bounds on the support of candidate itemsets. We show how the deduction rules allow for constructing a minimal representation for all frequent itemsets. We also present connections between our proposal and recent proposals for concise representations and we give the results of experiments on real-life datasets that show the effectiveness of the deduction rules. In fact, the experiments even show that in many cases, first mining the concise representation, and then creating the frequent itemsets from this representation outperforms existing frequent set mining algorithms.
    Original languageEnglish
    Title of host publicationPrinciples of data mining and knowledge iscovery : proceedings 6th European conference, PKDD 2002, Helsinki, Finland, august 19-23, 2002
    EditorsT. Elomaa, H. Mannila, H. Toivonen
    Place of PublicationBerlin
    ISBN (Print)3-540-44037-2
    Publication statusPublished - 2002

    Publication series

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


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