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    題名: Discovering Time-Interval Sequential Patterns by a Pattern Growth Approach with Confidence Constraints
    作者: Shyur, Huan-Jyh;Jou, Chi-Chang;Cheng, Chi-Bin;Yen, Chih-Yu
    關鍵詞: Sequential pattern mining;Time-interval sequential patterns;Pattern growth;Confidence
    日期: 2016-05-14
    上傳時間: 2016-12-03 02:11:28 (UTC+8)
    摘要: 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 traffic analysis. Conventional sequential pattern mining approaches generally focus only the orders of items; however, the time interval between two consecutive events can be a more 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. The current study suggests that the confidence of the occurrence of a pattern is also important other than the frequency (i.e. support) of the pattern. Thus, the proposed approach extracts a pattern by first satisfying a minimum confidence constraint, and then finds out the least time interval that satisfies the minimum support constraint. Experiments are conducted to evaluate the performance of the proposed approach.
    關聯: 論文集
    顯示於類別:[資訊管理學系暨研究所] 會議論文

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