A generalized approach to the suppressed fuzzy c-means algorithm

Authors:
László Szilágyi;Sándor M. Szilágyi;Csilla Kiss
Affiliations:
Sapientia-Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Tîrgu-Mureş, Romania and Budapest University of Technology and Economics, Department of Co ...;Sapientia-Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Tîrgu-Mureş, Romania;Sapientia-Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Tîrgu-Mureş, Romania and Babeş-Bolyai University of Cluj-Napoca, Romania and Faculty ...
Venue:
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Year:
2010

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Abstract

Suppressed fuzzy c-means (s-FCM) clustering was introduced with the intention of combining the higher convergence speed of hard c- means (HCM) clustering with the finer partition quality of fuzzy c-means (FCM) algorithm. Suppression modifies the FCM iteration by creating a competition among clusters: lower degrees of memberships are reduced via multiplication with a previously set constant suppression rate, while the largest fuzzy membership grows by swallowing all the suppressed parts of the small ones. Suppressing the FCM algorithm was found successful in terms of accuracy and working time. In this paper we introduce some generalized formulations of the suppression rule, leading to an infinite number of new clustering algorithms. Based on a large amount of numerical tests performed in multidimensional environment, some generalized forms of suppression proved to give more accurate partitions than FCM and s-FCM.