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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/109383


    Title: Discovering Time-Interval Sequential Patterns by a Pattern Growth Approach with Confidence Constraints
    Authors: Shyur, Huan-Jyh;Jou, Chichang;Cheng, Chi-Bin;Yen, Chih-Yu
    Keywords: Sequential pattern mining;time-interval sequential patterns;pattern growth;confidence
    Date: 2016
    Issue Date: 2017-01-17 11:27:44 (UTC+8)
    Publisher: 淡江大學出版中心
    Abstract: Sequential pattern mining is to discover frequent sequential patterns in a sequence database. The technique is applied to fields such as web click-stream mining, failure forecast, and traf- fic analysis. Conventional sequential pattern-mining approaches generally focus only the orders of items; however, the time interval between two consecutive events can be a valuable information when the time of the occurrence of an event is concerned. This study extends the concept of the well-known pattern growth approach, PrefixSpan algorithm, to propose a novel sequential pattern mining approach for sequential patterns with time intervals. Unlike the other time-interval sequential pattern-mining algorithms, the approach concerns the time
    for the next event to occur more than the timing information with its precedent events. To obtain a more reliable sequential pattern, a new measure of the confidence of a sequential pattern is defined. Experiments are conducted to evaluate the performance of the proposed approach.
    Relation: nternational Journal of Information and Management Sciences 27 (2), pp.129-145
    DOI: 10.6186/IJIMS.2016.27.2.4
    Appears in Collections:[資訊與管理科學期刊] 第27卷第2期

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