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TitleMining Sequential Patterns in Business-to-business(B2B) Environment
Creator唐揆
Hu, Ya-Han ; Chen, Yen-Liang ; Tang, Kwei
Contributor企管系
Key Wordsbusiness-to-business ; data mining ; sequential pattern
Date2009.12
Date Issued17-Apr-2014 12:39:09 (UTC+8)
SummarySequential pattern mining is a powerful data mining technique for finding time-related behaviour in sequence databases. In this paper, we focus on mining sequential patterns in the business-to-business (B2B) environment. Because customers’ sequences in the B2B environment are very long, and almost all items are frequently purchased by all customers, using traditional methods may result in a large number of uninteresting and meaningless patterns and a long computational time. To solve these problems, we introduce three conditions (constraints) — compactness, repetition, and recency — and consider them jointly with frequency in selecting sequential patterns. An efficient algorithm is developed to discover frequent sequential patterns which satisfy the conditions. Empirical results show that the proposed method is computationally efficient and effective in extracting useful sequential patterns in the B2B environment.
RelationJournal of Information Science,35(6),677-694
Typearticle
DOI http://dx.doi.org/10.1177/0165551509103600
dc.contributor 企管系en_US
dc.creator (作者) 唐揆zh_TW
dc.creator (作者) Hu, Ya-Han ; Chen, Yen-Liang ; Tang, Kweien_US
dc.date (日期) 2009.12en_US
dc.date.accessioned 17-Apr-2014 12:39:09 (UTC+8)-
dc.date.available 17-Apr-2014 12:39:09 (UTC+8)-
dc.date.issued (上傳時間) 17-Apr-2014 12:39:09 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/65454-
dc.description.abstract (摘要) Sequential pattern mining is a powerful data mining technique for finding time-related behaviour in sequence databases. In this paper, we focus on mining sequential patterns in the business-to-business (B2B) environment. Because customers’ sequences in the B2B environment are very long, and almost all items are frequently purchased by all customers, using traditional methods may result in a large number of uninteresting and meaningless patterns and a long computational time. To solve these problems, we introduce three conditions (constraints) — compactness, repetition, and recency — and consider them jointly with frequency in selecting sequential patterns. An efficient algorithm is developed to discover frequent sequential patterns which satisfy the conditions. Empirical results show that the proposed method is computationally efficient and effective in extracting useful sequential patterns in the B2B environment.en_US
dc.format.extent 260072 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Journal of Information Science,35(6),677-694en_US
dc.subject (關鍵詞) business-to-business ; data mining ; sequential patternen_US
dc.title (題名) Mining Sequential Patterns in Business-to-business(B2B) Environmenten_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1177/0165551509103600en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1177/0165551509103600 en_US