A fuzzy statistics based method for mining fuzzy correlation rules

Authors:
Nancy P. Lin;Hung-Jen Chen;Hao-En Chueh;Wei-Hua Hao;Chung-I Chang
Affiliations:
Department of Computer Science and Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.
Venue:
WSEAS Transactions on Mathematics
Year:
2007

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Abstract

Mining fuzzy association rules is the task of finding the fuzzy itemsets which frequently occur together in large fuzzy dataset, but most proposed methods may identify a fuzzy rule with two fuzzy itemsets as interesting when, in fact, the presence of one fuzzy itemsets in a record does not imply the presence of the other one in the same record. To prevent generating this kind of misleading fuzzy rule, in this paper, we construct a new method for finding relationships between fuzzy itemsets based on fuzzy statistics, and the generated rules are called fuzzy correlation rules. In our method, a fuzzy correlation analysis which can show us the strength and the type of the linear relationship between two fuzzy itemsets is used. By using thus fuzzy statistics analysis, the fuzzy correlation rules with the information about that two fuzzy not only frequently occur together in same records but also are related to each other can be generated.