Mining top-k frequent sequential pattern in item Interval extended sequence database / Tran Huy Duong, Nguyen Truong Thang, Vu Duc Thi, Tran Thế Anh - Tran, Huy Duong
Frequent sequential pattern mining in item interval extended sequence database (ỉSDB) has been one of the interesting tasks in recent years. Unlike classic frequent sequential pattern mining. The pattern mining in ISDB also consider the item interval between successive items thus it may extract more meaningful sequential patterns in real life. Most previous frequent sequential pattern mining in ISDB algorithms needs a minimum support threshold (min sup) to perform the mining . However, it’s not easy for use to provide an appropriate threshold in practice. The too high min sup value will lead to missing valuable patterns. while the too low min sup value may generate too many useless patterns. To address this problem we propose an algorithm: Top KWFP — top K weighted frequent sequential pattern mining in item interval extended sequence database. Our algorithm doesn't needs to provide a fixed min sup value. This min sup value will dynamically raise during the mining process.