Analytical and Numerical Evaluation of the Suppressed Fuzzy C-Means Algorithm

Authors:
László Szilágyi;Sándor M. Szilágyi;Zoltán Benyó
Affiliations:
Faculty of Technical and Human Science, Târgu-Mureş, Sapientia - Hungarian Science University of Transylvania, Romania and Department of Control Engineering and Information Technology, B ...;Faculty of Technical and Human Science, Târgu-Mureş, Sapientia - Hungarian Science University of Transylvania, Romania;Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
Venue:
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
Year:
2008

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Abstract

Suppressed fuzzy c-means (s-FCM) clustering was introduced in [Fan, J. L., Zhen, W. Z., Xie, W. X.: Suppressed fuzzy c-means clustering algorithm. Patt. Recogn. Lett. 24, 1607---1612 (2003)] with the intention of combining the higher speed of hard c-means (HCM) clustering with the better classification properties of fuzzy c-means (FCM) algorithm. They modified the FCM iteration to create a competition among clusters: lower degrees of memberships were diminished according to a previously set suppression rate, while the largest fuzzy membership grew by swallowing all the suppressed parts of the small ones. Suppressing the FCM algorithm was found successful in the terms of accuracy and working time, but the authors failed to answer a series of important questions. In this paper we clarify the view upon the optimality and the competitive behavior of s-FCM via analytical computations and numerical analysis.