A rough set-based fuzzy clustering

Authors:
Zhao Yaqin;Zhou Xianzhong;Tang Guizhong
Affiliations:
Department of Automation, Nanjing University of Science & Technology, Nanjing, China;School of Management and Engineering, Nanjing University, Nanjing, China;School of Automation, Nanjing University of Technology, Nanjing, China
Venue:
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Year:
2005

Citing 3
Cited 1

Probabilistic models in cluster analysis

Computational Statistics & Data Analysis - Special issue on classification
Data mining: concepts and techniques

Data mining: concepts and techniques
A Clustering Method for Nominal and Numerical Data Based on Rough Set Theory

Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence

Review:

The Knowledge Engineering Review

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Abstract

This paper presents a rough set-based fuzzy clustering algorithm in which the objects of fuzzy clustering are initial clusters obtained in terms of equivalence relations. Initial clustering is performed directly by judging whether equivalence relations are equal, not computing the intersection of equivalence classes as usual, and the correctness of the theory is proved using rough set theory. Excessive generation of some small classes is suppressed by secondary clustering on the basis of defining fuzzy similarity between two initial clusters. Consequently the dimension of fuzzy similarity matrix is reduced. The definition of integrated approximation precision is given as evaluation of clustering validity. The algorithm can dynamically adjust parameter to get the optimal result. Some experiments were performed to validate this method. The results showed that the algorithm could handle preferably the clustering problems of both numerical data and nominal data.