Efficient mining of frequent sequence generators
Chuancong Gao, Jianyong Wang, Yukai He, Lizhu Zhou · 2008
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. Ex-perimental results show that FEAT is more efficient than traditional sequential pattern mining algorithms but generates more concise re-sult set, and is very effective for classifying Web product reviews.