Efficient mining of frequent sequence generators

Authors:
Chuancong Gao;Jianyong Wang;Yukai He;Lizhu Zhou
Affiliations:
Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China
Venue:
Proceedings of the 17th international conference on World Wide Web
Year:
2008

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Abstract

Sequential pattern mining has raised great interest in data mining research field in recent years. However, to our best knowledge, no existing work studies the problem of frequent sequence generator mining. In this paper we present a novel algorithm, FEAT (abbr. Frequent sEquence generATor miner), to perform this task. Experimental results show that FEAT is more efficient than traditional sequential pattern mining algorithms but generates more concise result set, and is very effective for classifying Web product reviews.