A novel mining algorithm for periodic clustering sequential patterns

Che Lun Hung*, Don Lin Yang, Yeh Ching Chung, Ming Chuan Hung

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

In knowledge discovery, data mining of time series data has many important applications. Especially, sequential patterns and periodic patterns, which evolved from the association rule, have been applied in many useful practices. This paper presents another useful concept, the periodic clustering sequential (PCS) pattern, which uses clustering to mine valuable information from temporal or serially ordered data in a period of time. For example, one can cluster patients according to symptoms of the illness under study, but this may just result in several clusters with specific symptoms for analyzing the distribution of patients. Adding time series analysis to the above investigation, we can examine the distribution of patients over the same or different seasons. For policymakers, the PCS pattern is more useful than traditional clustering result and provides a more effective support of decision-making.

原文English
主出版物標題Advances in Applied Artificial Intelligence - 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Proceedings
發行者Springer Verlag
頁面1299-1308
頁數10
ISBN(列印)3540354530, 9783540354536
DOIs
出版狀態Published - 2006
事件19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006 - Annecy, France
持續時間: 27 6月 200630 6月 2006

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4031 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006
國家/地區France
城市Annecy
期間27/06/0630/06/06

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