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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 | |
Đề mục chủ đề
| Sequential pattern | |
Thuật ngữ không kiểm soát
| Time | |
Thuật ngữ không kiểm soát
| Weighted | |
Thuật ngữ không kiểm soát
| Item interval | |
Thuật ngữ không kiểm soát
| Top-K | |
Thuật ngữ không kiểm soát
| Mô hình tuần tự | |
Thuật ngữ không kiểm soát
| 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 |
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| 520 | |aFrequent 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 |
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