Non-derivable itemset mining

T. Calders, B. Goethals

    Research output: Contribution to journalArticleAcademicpeer-review

    96 Citations (Scopus)

    Abstract

    All frequent itemset mining algorithms rely heavily on the monotonicity principle for pruning. This principle allows for excluding candidate itemsets from the expensive counting phase. In this paper, we present sound and complete deduction rules to derive bounds on the support of an itemset. Based on these deduction rules, we construct a condensed representation of all frequent itemsets, by removing those itemsets for which the support can be derived, resulting in the so called Non-Derivable Itemsets (NDI) representation. We also present connections between our proposal and recent other proposals for condensed representations of frequent itemsets. Experiments on real-life datasets show the effectiveness of the NDI representation, making the search for frequent non-derivable itemsets a useful and tractable alternative to mining all frequent itemsets.
    Original languageEnglish
    Pages (from-to)171-206
    JournalData Mining and Knowledge Discovery
    Volume14
    Issue number1
    DOIs
    Publication statusPublished - 2007

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