An efficient algorithm for mining time interval-based patterns in large databases

Yi Cheng Chen*, Ji Chiang Jiang, Wen-Chih Peng, Suh Yin Lee

*Corresponding author for this work

研究成果: Conference contribution同行評審

43 引文 斯高帕斯(Scopus)

摘要

Most studies on sequential pattern mining are mainly focused on time point-based event data. Few research efforts have elaborated on mining patterns from time interval-based event data. However, in many real applications, event usually persists for an interval of time. Since the relationships among event time intervals are intrinsically complex, mining time interval-based patterns in large database is really a challenging problem. In this paper, a novel approach, named as incision strategy and a new representation, called coincidence representation are proposed to simplify the processing of complex relations among event intervals. Then, an efficient algorithm, CTMiner (Coincidence Temporal Miner) is developed to discover frequent time-interval based patterns. The algorithm also employs two pruning techniques to reduce the search space effectively. Furthermore, experimental results show that CTMiner is not only efficient and scalable but also outperforms state-of-the-art algorithms.

原文English
主出版物標題CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops
頁面49-58
頁數10
DOIs
出版狀態Published - 1 十二月 2010
事件19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10 - Toronto, ON, Canada
持續時間: 26 十月 201030 十月 2010

出版系列

名字International Conference on Information and Knowledge Management, Proceedings

Conference

Conference19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
國家Canada
城市Toronto, ON
期間26/10/1030/10/10

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