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

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

 Trần, Huy Dương
 2018.
 tr. 249-263 Tiếng Việt
Sequential patternTimeWeightedItem intervalTop-KMô hình tuần tựTrọng số
Tác giả CN Trần, Huy Dương
Nhan đề Mining top-k frequent sequential pattern in item interval extended sequence database / Trần Huy Dương...
Thông tin xuất bản 2018.
Mô tả vật lý tr. 249-263
Tóm tắt Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of interesting task 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 (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
Thuật ngữ chủ đề Sequential pattern
Từ khóa tự do Time
Từ khóa tự do Weighted
Từ khóa tự do Item interval
Từ khóa tự do Top-K
Từ khóa tự do Mô hình tuần tự
Từ khóa tự do Trọng số
Tác giả(bs) CN Nguyễn, Trường Thăng
Tác giả(bs) CN Trần, Thế Anh
Tác giả(bs) CN Vũ, Thị Đức
Nguồn trích Tạp chí Tin học và Điều khiển học- Vol.34, No 3
MARC
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245[1 0] |a Mining top-k frequent sequential pattern in item interval extended sequence database / |c Trần Huy Dương...
260[ ] |c 2018.
300[1 0] |a tr. 249-263
520[ ] |a Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of interesting task 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 (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
650[1 0] |a Sequential pattern
653[0 ] |a Time
653[0 ] |a Weighted
653[0 ] |a Item interval
653[0 ] |a Top-K
653[0 ] |a Mô hình tuần tự
653[0 ] |a Trọng số
700[0 ] |a Nguyễn, Trường Thăng
700[0 ] |a Trần, Thế Anh
700[0 ] |a Vũ, Thị Đức
773[0 ] |t Tạp chí Tin học và Điều khiển học |g Vol.34, No 3
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