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  • Ký hiệu PL/XG: 005
    Nhan đề: Mining top-k frequent sequential pattern in item Interval extended sequence database /
DDC 005
Tác giả CN Tran, Huy Duong
Nhan đề Mining top-k frequent sequential pattern in item Interval extended sequence database / Tran Huy Duong, Nguyen Truong Thang, Vu Duc Thi, Tran Thế Anh
Mô tả vật lý 16 p.
Tóm tắt 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.
Từ khóa tự do Cơ sở dữ liệu chuỗi mở rộng
Từ khóa tự do Item interval
Từ khóa tự do Sequential pattern
Tác giả(bs) CN Nguyen, Truong Thang
Tác giả(bs) CN Tran, The Anh
Tác giả(bs) CN Vu, Duc Thi
Nguồn trích Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics 2018Pages 249 - 263 Số: 03 Tập: 34
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100 |aTran, Huy Duong
245 |aMining top-k frequent sequential pattern in item Interval extended sequence database / |cTran Huy Duong, Nguyen Truong Thang, Vu Duc Thi, Tran Thế Anh
300 |a16 p.
520 |aFrequent 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.
653 |aCơ sở dữ liệu chuỗi mở rộng
653 |aItem interval
653 |aSequential pattern
700 |aNguyen, Truong Thang
700 |aTran, The Anh
700 |aVu, Duc Thi
773 |tTạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics |d2018|gPages 249 - 263|v34|i03
890|c0|a0|b0|d0
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