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    題名: A data mining approach to discovering reliable sequential patterns
    作者: Shyur, Huan-Jyh;Jou, Chichang;Chang, Keng
    貢獻者: 淡江大學資訊管理學系
    關鍵詞: Data mining;Sequential patterns;Inter-arrival time probability
    日期: 2013-08
    上傳時間: 2013-10-15 23:22:22 (UTC+8)
    出版者: New York: Elsevier Inc.
    摘要: Sequential pattern mining is a data mining method for obtaining frequent sequential patterns in a sequential database. Conventional sequence data mining methods could be divided into two categories: Apriori-like methods and pattern growth methods. In a sequential pattern, probability of time between two adjacent events could provide valuable information for decision-makers. As far as we know, there has been no methodology developed to extract this probability in the sequential pattern mining process. We extend the PrefixSpan algorithm and propose a new sequential pattern mining approach: P-PrefixSpan. Besides minimum support-count constraint, this approach imposes minimum time-probability constraint, so that fewer but more reliable patterns will be obtained. P-PrefixSpan is compared with PrefixSpan in terms of number of patterns obtained and execution efficiency. Our experimental results show that P-PrefixSpan is an efficient and scalable method for sequential pattern mining.
    關聯: Journal of Systems and Software 86(8), p.2196–2203
    DOI: 10.1016/j.jss.2013.03.105
    顯示於類別:[資訊管理學系暨研究所] 期刊論文

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