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

Paper:

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

Received:
November 7, 2006
Accepted:
August 9, 2007
Published:
December 20, 2007
Keywords:
fuzzy set, feature significance, clustering
Abstract

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:
Jianchao Han and Mohsen 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.
Data files:
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