  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. This article was published in Data Mining and
Knowledge Discovery and is accessible at
http://dx.doi.org/10.1007/s10618-016-0467-9.
