Skopus: Exact discovery of the most interesting sequential patterns under Leverage. (arXiv:1506.08009v2 [cs.AI] UPDATED)

This paper presents a framework for exact discovery of the most interesting sequential patterns. 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 is based on comparing the pattern support with the average support of its sister patterns, obtained by permuting (to certain extent) the items of the pattern. The larger the support compared to the expectation, 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.

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