Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

Jia Mei Guo; Yin Xiang Pei

doi:10.4028/www.scientific.net/AMR.998-999.842

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 998-999Association Rules Mining Based on Adaptive Fuzzy...

Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

Abstract:

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

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Periodical:

Advanced Materials Research (Volumes 998-999)

Pages:

842-845

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.998-999.842

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Online since:

July 2014

Authors:

Jia Mei Guo*, Yin Xiang Pei

Keywords:

Association Rule, Fuzzy Clustering, Fuzzy Partition

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* - Corresponding Author

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