GeCiM: A Novel Generalized Approach to C-Means Clustering

Authors:
László Szilágyi;David Iclănzan;Sándor M. Szilágyi;Dan Dumitrescu
Affiliations:
Sapientia, Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Târgu-Mureş, Romania and Department of Control Engineering and Information Technology, Bu ...;Sapientia, Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Târgu-Mureş, Romania and Faculty of Mathematics and Computer Science, Babeş-Bolyai U ...;Sapientia, Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Târgu-Mureş, Romania;Faculty of Mathematics and Computer Science, Babeş-Bolyai University of Cluj-Napoca, Romania
Venue:
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Year:
2008

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Abstract

All three conventional c-means clustering algorithms have their advantages and disadvantages. This paper presents a novel generalized approach to c-means clustering: the objective function is considered to be a mixture of the FCM, PCM, and HCM objective functions. The optimal solution is obtained via evolutionary computation. Our main goal is to reveal the properties of such mixtures and to formulate some rules that yield accurate partitions.