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| 題 名 | Discovering Time-Interval Sequential Patterns by a Pattern Growth Approach with Confidence Constraints=利用考量信賴度限制的樣式成長方法發掘具時間間隔循序特徵樣式 |
|---|---|
| 作 者 | 徐煥智; 周清江; 鄭啓斌; 顏志祐; | 書刊名 | International Journal of Information and Management Sciences |
| 卷 期 | 27:2 2016.06[民105.06] |
| 頁 次 | 頁a9+129-145 |
| 分類號 | 028.7 |
| 關鍵詞 | 循序樣式探勘; 時間區間循序式; 樣式成長; 信賴度; Sequential pattern mining; Time-interval sequential patterns; Pattern growth; Confidence; |
| 語 文 | 英文(English) |
| 英文摘要 | 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 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. |
本系統中英文摘要資訊取自各篇刊載內容。