Mining top-K frequent sequential pattern in item interval extended sequence database

Abstract

Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of the interesting tasks in recent years. Unlike classic frequent sequential pattern mining, the pattern mining in iSDB also considers the item interval between successive items; thus, it may extract more meaningful sequential patterns in real life. Most previous frequent sequential pattern mining in iSBD algorithms needs a minimum support threshold (minsup) to perform the mining. However, it’s not easy for users to provide an appropriate threshold in practice. The too high minsup value will lead to missing valuable patterns, while the too low minsup value may generate too many useless patterns. To address this problem, we propose an algorithm: TopKWFP – top – K weighted frequent sequential pattern mining in item interval extended sequence database. Our algorithm doesn’t need to provide a fixed minsup value, this minsup value will dynamically raise during the mining process.