An efficient GA-Based algorithm for mining negative sequential patterns

Authors:
Zhigang Zheng;Yanchang Zhao;Ziye Zuo;Longbing Cao
Affiliations:
Data Sciences & Knowledge Discovery Research Lab, Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering & IT, University of Technology, Sydney, Australia;Data Sciences & Knowledge Discovery Research Lab, Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering & IT, University of Technology, Sydney, Australia;Data Sciences & Knowledge Discovery Research Lab, Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering & IT, University of Technology, Sydney, Australia;Data Sciences & Knowledge Discovery Research Lab, Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering & IT, University of Technology, Sydney, Australia
Venue:
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2010

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Abstract

Negative sequential pattern mining has attracted increasing concerns in recent data mining research because it considers negative relationships between itemsets, which are ignored by positive sequential pattern mining. However, the search space for mining negative patterns is much bigger than that for positive ones. When the support threshold is low, in particular, there will be huge amounts of negative candidates. This paper proposes a Genetic Algorithm (GA) based algorithm to find negative sequential patterns with novel crossover and mutation operations, which are efficient at passing good genes on to next generations without generating candidates. An effective dynamic fitness function and a pruning method are also provided to improve performance. The results of extensive experiments show that the proposed method can find negative patterns efficiently and has remarkable performance compared with some other algorithms of negative pattern mining.