Exact discovery of the most interesting sequential patterns

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. We carry out experiments on both synthetic data with known patterns and real-world datasets; both experiments confirm the consistency and relevance of our approach.