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並列摘要


The mining of closed sequential patterns has attracted researchers for its capability of using compact results to preserving the same expressive power as traditional mining. Many studies have shown that constraints are essential for applications of sequential patterns. However, time constraints have not been incorporated into closed sequence mining yet. Therefore, we propose an algorithm called CTSP for closed sequential pattern mining with time constraints. CTSP loads the database into memory and constructs time-indexes to facilitate both pattern mining and closure checking, within the pattern growth framework. The index sets are utilized to efficiently mine the patterns without generating any candidate or sub-database. The bidirectional closure checking strategy further speeds up the mining. The comprehensive experiments with both synthetic and real datasets show that CTSP efficiently mines closed sequential patterns satisfying the time constraints, and has good linear scalability with respect to the database size.

被引用紀錄


周濬森(2013)。以動態時間校準法探討全身控制之虛擬實境學習系統〔碩士論文,長榮大學〕。華藝線上圖書館。https://doi.org/10.6833/CJCU.2013.00057
Chang, C. Y. (2014). 期間限制探勘於高效用序列樣式 [master's thesis, National Chung Cheng University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0033-2110201614003611

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