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Parallel mining and application of fuzzy association rules |
| LU Jian-jiang1, ZOU Xiao-feng1, XU Bao-wen2, KANG Da-zhou2, LI Yan-hui2, ZHOU Jin2 |
| 1.Institute of Command Automation, PLA University of Science and Technology, Nanjing 210007, China; 2.Department of Computer Science and Engineering, Southeast University, Nanjing 210096, China; |
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Abstract Quantitative attributes are partitioned into several fuzzy sets by using fuzzy c-means algorithm. Fuzzy c-means algorithm can embody the actual distribution of the data, and fuzzy sets can soften the partition boundary. Then, we improve the search technology of apriori algorithm and present the algorithm for mining fuzzy association rules. As the database size becomes larger and larger, a better way is to mine fuzzy association rules in parallel. In the parallel mining algorithm, quantitative attributes are partitioned into several fuzzy sets by using parallel fuzzy c-means algorithm. Boolean parallel algorithm is improved to discover frequent fuzzy attribute set, and the fuzzy association rules with at least a minimum confidence are generated on all processors. The experiment results implemented on the distributed linked PC/workstation show that the parallel mining algorithm has fine scaleup, sizeup and speedup. Last, we discuss the application of fuzzy association rules in the classification. The example shows that the accuracy of classification systems of the fuzzy association rules is better than that of the two popular classification methods: C4.5 and CBA.
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Issue Date: 05 June 2006
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