JACIII Vol.11 No.10 pp. 1204-1208
doi: 10.20965/jaciii.2007.p1204


An Iterative Approach for Fuzzy Clustering Based on Feature Significance

Jianchao Han and Mohsen Beheshti

Department of Computer Science, California State University Dominguez Hills, 1000 E. Victoria St. Carson, CA 90747 USA

November 7, 2006
August 9, 2007
December 20, 2007
fuzzy set, feature significance, clustering
Clustering is a technique to group a set of unsupervised data based on the conceptual clustering principle: maximizing the intraclass similarity and minimizing the interclass similarity. Existing clustering approaches concentrate in the different data types and assume that all features play the same role in algorithm validations. However, some features may be more significant than others in forming clusters. In this paper, we consider the feature significance and include it in the clustering algorithms. An iterative approach for fuzzy clustering based on the feature significance is presented and applied in the k-means algorithm for numerical data, the k-modes algorithm for categorical data, and the k-prototypes algorithm for mixed data.
Cite this article as:
J. Han and M. Beheshti, “An Iterative Approach for Fuzzy Clustering Based on Feature Significance,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.10, pp. 1204-1208, 2007.
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