A Cluster-Based Method for Mining Generalized Fuzzy Association Rules

Authors:
Hung-Pin Chiu;Yi-Tsung Tang;Kun-Lin Hsieh
Affiliations:
NAN HUA University, Taiwan R.O.C.;NAN HUA University, Taiwan R.O.C.;National Taitung University, Taiwan, R.O.C.
Venue:
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
Year:
2006

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Exploring fuzzy ontologies in mining generalized association rules

ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III

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Abstract

The discovery of generalized fuzzy association rules is a very important data-mining task, because more general and qualitative knowledge can be uncovered for decision making. In the literature, few algorithms have been proposed for such a problem, moreover, the efficiency of these algorithms needs to be improved to handle real-world large datasets. In this paper, we present an efficient method named cluster-based fuzzy association rule (CBFAR). The CBFAR method creates cluster-based fuzzy-sets tables by scanning the database once, and then clustering the transaction records to the k-th cluster table, where the length of a record is k. Based on the information stored in the table, less contrast and database scans are required to generate large itemsets. Experimental results show that CBFAR outperforms a known Apriori-based fuzzy association rules mining algorithm.