CEMiner - An efficient algorithm for mining closed patterns from time interval-based data

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

*Corresponding author for this work

研究成果: Conference contribution同行評審

27 引文 斯高帕斯(Scopus)

摘要

The mining of closed sequential patterns has attracted researchers for its capability of using compact results to preserve the same expressive power as conventional mining. However, existing studies only focus on time point-based data. Few research efforts have elaborated on discovering closed sequential patterns from time interval-based data, where each data persists for a period of time. Mining closed time intervalbased patterns, also called closed temporal patterns, is an arduous problem since the pairwise relationships between two interval-based events are intrinsically complex. In this paper, an efficient algorithm, CEMiner is developed to discover closed temporal patterns from interval-based data. Algorithm CEMiner employs some optimization techniques to effectively reduce the search space. The experimental results on both synthetic and real datasets indicate that CEMiner not only significantly outperforms the prior interval-based mining algorithms in terms of execution time but also possesses graceful scalability. The experiment conducted on real dataset shows the practicability of time interval-based closed pattern mining.

原文English
主出版物標題Proceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
頁面121-130
頁數10
DOIs
出版狀態Published - 1 十二月 2011
事件11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, Canada
持續時間: 11 十二月 201114 十二月 2011

出版系列

名字Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN(列印)1550-4786

Conference

Conference11th IEEE International Conference on Data Mining, ICDM 2011
國家Canada
城市Vancouver, BC
期間11/12/1114/12/11

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