MSGPs: a novel algorithm for mining sequential generator patterns

Authors:
Thi-Thiet Pham;Jiawei Luo;Tzung-Pei Hong;Bay Vo
Affiliations:
School of Information Science and Engineering, Hunan University, Changsha City, Hunan Province, Republic of China, Faculty of Information Technology, Ho Chi Minh City University of Industry, Ho Ch ...;School of Information Science and Engineering, Hunan University, Changsha City, Hunan Province, Republic of China;Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, R.O.C.,Department of Computer Science and Engineering, National Sun Yat-sen Univers ...;Information Technology College, Ho Chi Minh, Vietnam
Venue:
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
Year:
2012

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Abstract

Sequential generator pattern mining is an important task in data mining. Sequential generator patterns used together with closed sequential patterns can provide additional information that closed sequential patterns alone are not able to provide. In this paper, we proposed an algorithm called MSGPs, which based on the characteristics of sequential generator patterns and sequence extensions by doing depth-first search on the prefix tree, to find all of the sequential generator patterns. This algorithm uses a vertical approach to listing and counting the support, based on the prime block encoding approach of the prime factorization theory to represent candidate sequences and determine the frequency for each candidate. Experimental results showed that the proposed algorithm is effective.