Skopus: Mining top-k sequential patterns under leverage

François Petitjean, Tao Li, Nikolaj Tatti, Geoffrey I. Webb

    Research output: Contribution to journalArticleResearchpeer-review

    36 Citations (Scopus)


    This paper presents a framework for exact discovery of the top-k sequential patterns under Leverage. It combines (1) a novel definition of the expected support for a sequential pattern—a concept on which most interestingness measures directly rely—with (2) Skopus: a new branch-and-bound algorithm for the exact discovery of top-k sequential patterns under a given measure of interest. Our interestingness measure employs the partition approach. A pattern is interesting to the extent that it is more frequent than can be explained by assuming independence between any of the pairs of patterns from which it can be composed. The larger the support compared to the expectation under independence, the more interesting is the pattern. We build on these two elements to exactly extract the k sequential patterns with highest leverage, consistent with our definition of expected support. We conduct experiments on both synthetic data with known patterns and real-world datasets; both experiments confirm the consistency and relevance of our approach with regard to the state of the art.
    Original languageEnglish
    Pages (from-to)1086-1111
    Number of pages26
    JournalData Mining and Knowledge Discovery
    Issue number5
    Publication statusPublished - 1 Sept 2016


    • Data mining
    • Pattern mining
    • Sequential data
    • Exact discovery
    • Interestingness measures

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