On Objective-Based Rough Clustering with Fuzzy-Set Representation

Author:

Kinoshita Naohiko, ,Endo Yasunori,Onishi Ken, ,

Abstract

The rough clustering algorithm we proposed based on the optimization of objective function (RCM) has a problem because conventional rough clustering algorithm results do not ensure that solutions are optimal. To solve this problem, we propose rough clustering algorithms based on optimization of an objective function with fuzzy-set representation. This yields more flexible results than RCM. We verify algorithm effectiveness through numerical examples.

Publisher

Fuji Technology Press Ltd.

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction

Reference15 articles.

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2.  P. Lingras and G. Peters, “Rough clustering,” WIREs Data Mining and Knowledge Discovery, Vol.1, Issue 1, pp. 64-72, 2011.

3.  Z. Pawlak, “Rough Sets,” Int. J. of Computer and Information Sciences, Vol.11, No.5, pp. 341-356, 1982.

4.  P. Lingrs and C. West, “Interval Set Clustering of Web Users with RoughK-Means,” J. of Intelligent Information Systems, Vol.23, No.1, pp. 5-16, 2004.

5.  P. Maji and S. K. Pal, “Rough Set based Generalized FuzzyC-Means Algorithm and Quantitative Indices,” IEEE Trans. on Systems, Man, and Cybernetics – Part B: Cybernetics, Vol.37, No.6, pp. 1529-1540, 2007.

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