TSP: Mining top-k closed sequential patterns

Abstract

Sequential pattern mining has been studied extensively in the data mining community. Most previous studies require the specification of a min_support threshold for mining a complete set of sequential patterns satisfying the threshold. However, in practice, it is difficult for users to provide an appropriate min_support threshold. To overcome this difficulty, we propose an alternative mining task: mining top-k frequent closed sequential patterns of length no less than min_l, where k is the desired number of closed sequential patterns to be mined and min_l is the minimal length of each pattern. We mine the set of closed patterns because it is a compact representation of the complete set of frequent patterns. An efficient algorithm, called TSP, is developed for mining such patterns without min_support. Starting at (absolute) min_support=1, the algorithm makes use of the length constraint and the properties of top-k closed sequential patterns to perform dynamic support raising and projected database pruning. Our extensive performance study shows that TSP has high performance. In most cases, it outperforms the efficient closed sequential pattern-mining algorithm, CloSpan, even when the latter is running with the best tuned min_support threshold. Thus, we conclude that, for sequential pattern mining, mining top-k frequent closed sequential patterns without min_support is more preferable than the traditional min_support-based mining.