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    題名: Discover Sequential Patterns in Incremental Database
    作者: Lin, Nancy P.;Hao, Wei Hua;Chen, Hung Jen;Chueh, Hao En;Chang, Chung I
    貢獻者: 淡江大學資訊工程學系;軍訓室
    關鍵詞: Data mining;Sequential patterns;condensed representations;Maximal sequential patterns
    日期: 2007-11
    上傳時間: 2013-06-07 10:47:25 (UTC+8)
    出版者: Braga: North Atlantic University Union
    摘要: The task of sequential pattern mining is to discover the complete set of sequential patterns in a given sequence database with minimum support threshold. But in practice, minimum support some time is defined afterward, or need to be adjusted to discover information that interest to knowledge workers. In the same time, the problem of discover sequential patterns in a incremental database is an essential issue in real world practice of datamining. This paper discusses the issue of maintaining discovered sequential patterns when some information is appended to a sequence database. Many previous works based on Apriori-like approaches are not capable to do so without re-running previously presented algorithms on the whole updated database. We propose a novel algorithm, called DSPID, which takes full advantage of the information obtained from previous mining results to cut down the cost of finding new sequential patterns in an incremental database.
    關聯: International Journal of Computers 4(1), pp.197-201
    DOI: 
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