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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/92477

    Title: A data mining approach to discovering reliable sequential patterns
    Authors: Shyur, Huan-Jyh;Jou, Chichang;Chang, Keng
    Contributors: 淡江大學資訊管理學系
    Keywords: Data mining;Sequential patterns;Inter-arrival time probability
    Date: 2013-08
    Issue Date: 2013-10-15 23:22:22 (UTC+8)
    Publisher: New York: Elsevier Inc.
    Abstract: 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.
    Relation: Journal of Systems and Software 86(8), p.2196–2203
    DOI: 10.1016/j.jss.2013.03.105
    Appears in Collections:[Graduate Institute & Department of Information Management] Journal Article

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