Skopus: Mining top-k sequential patterns under leverage

TL;DR

This paper introduces SkOPUS, a novel algorithm for exactly discovering the top-k sequential patterns based on leverage, using a new expected support measure that accounts for independence assumptions.

Contribution

It proposes a new definition of expected support and a branch-and-bound algorithm for top-k sequential pattern mining under leverage, advancing the state of the art.

Findings

Experiments confirm the effectiveness of the approach on synthetic and real data.

The method outperforms existing techniques in pattern relevance and consistency.

The framework reliably identifies interesting sequential patterns based on leverage.

Abstract

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.