A modified fuzzy c-means algorithm for association rules clustering

Authors:
Dechang Pi;Xiaolin Qin;Peisen Yuan
Affiliations:
College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China;College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China;College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

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Abstract

The Fuzzy C-Means (FCM) algorithm is commonly used for clustering. It is one of the problems in association rules mining that a great number of rules generated from the dataset makes it difficult to analyze and use. From the angle of knowledge management, a modified FCM algorithm is proposed and applied to association rules clustering, which partitions these rules into the given classes by the attribute's weight based on information gain for evaluating the attribute's importance. Experiment with the UCI dataset shows that this algorithm can efficiently cluster the association rules for a user to understand.