Mining negative fuzzy sequential patterns

Authors:
Nancy P. Lin;Hung-Jen Chen;Wei-Hua Hao;Hao-En Chueh;Chung-I Chang
Affiliations:
Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan, R.O.C. and Department of Industrial Engineering and Management, St. John's University, Taipei, Taiwa ...;Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan, R.O.C.
Venue:
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Year:
2007

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Abstract

Many methods have been proposed for mining fuzzy sequential patterns. However, most of conventional methods only consider the occurrences of fuzzy itemsets in sequences. The fuzzy sequential patterns discovered by these methods are called as positive fuzzy sequential patterns. In practice, the absences of frequent fuzzy itemsets in sequences may imply significant information. We call a fuzzy sequential pattern as a negative fuzzy sequential pattern, if it also expresses the absencesof fuzzy itemsets in a sequence. In this paper, we proposed a method for mining negative fuzzy sequential patterns, called NFSPM. In our method, the absences of fuzzy itemsets are also considered. Besides, only sequences with high degree of interestingness can be selected as negative fuzzy sequential patterns. An example was taken to illustrate the process of the algorithm NFSPM. The result showed that our algorithm could prune a lot of redundant candidates, and could extract meaningful fuzzy sequential patterns from a large number of frequent sequences.