CCSpan: Mining closed contiguous sequential patterns

Abstract

Existing closed sequential pattern mining generates a more compact yet complete resulting set compared with general sequential pattern mining. However, conventional closed sequential pattern mining algorithms pose a great challenge at spawning a large number of inefficient and redundant patterns, especially when using low support thresholds or pattern-enriched databases. Driven by wide applications of sequential patterns with contiguous constraint, we propose CCSpan (Closed Contiguous Sequential pattern mining), an efficient algorithm for mining closed contiguous sequential patterns, which contributes to a much more compact pattern set but with the same information w.r.t. closed sequential patterns. Moreover, with the shorter feature of patterns, the closed contiguous sequential patterns are preferred for feature selection and sequence classification based on the Minimum Description Length principle. CCSpan adopts a novel snippet-growth paradigm to generate a series of snippets as candidates, each of which is attached with a set of item(s) that precisely record the pattern’s occurrences in the database, and CCSpan leverages three pruning techniques to improve the computational efficiency significantly. Our experiments based on both sparse and dense datasets demonstrated that CCSpan is efficient and scalable in terms of both database size and support threshold.