Abstract
Sequential pattern mining is an important data mining problem with broad applications. While the current methods are inducing sequential patterns within a single attribute, the proposed method is able to detect them among different attributes. By incorporating the additional attributes, the sequential patterns found are richer and more informative to the user. This paper proposes a new method for inducing multi-dimensional sequential patterns with the use of Hellinger entropy measure. A number of theorems are proposed to reduce the computational complexity of the sequential pattern systems. The proposed method is tested on some synthesized transaction databases.
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Dr. Chang-Hwan Lee is a full professor at the Department of Information and Communications at DongGuk University, Seoul, Korea since 1996. He has received his B.Sc. and M.Sc in Computer Science and Statistics from Seoul National University in 1982 and 1988, respectively. He received his Ph.D. in Computer Science and Engineering from University of Connecticut in 1994. Prior to joining DongGuk University in Korea, he had worked for AT&T Bell Laboratories, Middletown, USA. (1994-1995). He also had been a visiting professor at the University of Illinois at Urbana-Champaign (2000-2001). He is author or co-author of more than 50 refereed articles on topics such as machine learning, data mining, artificial intelligence, pattern recognition, and bioinformatics.
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Lee, CH. IMSP: An information theoretic approach for multi-dimensional sequential pattern mining. Appl Intell 26, 231–242 (2007). https://doi.org/10.1007/s10489-006-0016-0
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DOI: https://doi.org/10.1007/s10489-006-0016-0