Alternative adaptive fuzzy C-means clustering

Authors:
Somchai Champathong;Sartra Wongthanavasu;Khamron Sunat
Affiliations:
Department of Computer Science, Faculty of Science, Khon Kaen University, Thailand;Department of Computer Science, Faculty of Science, Khon Kaen University, Thailand;Department of Computer Engineering, Faculty of Engineering, Mahanakorn University of technology, Thailand
Venue:
EC'06 Proceedings of the 7th WSEAS International Conference on Evolutionary Computing
Year:
2006

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Abstract

-Fuzzy C-Means (FCM) clustering algorithm is used in a variety of application domains. Fundamentally, it cannot be used for the subsequent data (adaptive data). A complete dataset has to be static prior to implementing the algorithm. This paper presents an alternative adaptive FCM which is able to cope with this limitation. The adaptive FCM using Euclidean and Mahalanobis distances were compared to alternative adaptive FCM for performance evaluation purposes. Two different datasets were taken into consideration for the compared test. In this respect, adaptive FCM using Euclidean and Mahalanobis distances results in more misclassified data. By implementing synthesis dataset with outlier, adaptive FCM using Euclidean and Mahalanobis distances give 9% and 14% of misclassification, respectively. While implemented in alternative adaptive FCM the proposed method exhibits the promising performance by giving 2% of misclassification. This result shows similar manner for carrying out in iris dataset.