A fast fuzzy clustering algorithm

Authors:
Moh'd Belal Al-Zoubi;Amjad Hudaib;Bashar Al-Shboul
Affiliations:
Department of Computer Information Systems, University of Jordan, Amman, Jordan;Department of Computer Information Systems, University of Jordan, Amman, Jordan;Department of Computer Information Systems, University of Jordan, Amman, Jordan
Venue:
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
Year:
2007

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Abstract

Clustering algorithms have been utilized in a wide variety of application areas. One of these algorithms is the Fuzzy C-Means algorithm (FCM). One of the problems with these algorithms is the time needed to converge. In this paper, a Fast Fuzzy C-Means algorithm (FFCM) is proposed based on experimentations, for improving fuzzy clustering. The algorithm is based on decreasing the number of distance calculations by checking the membership value for each point and eliminating those points with a membership value smaller than a threshold value. We applied FFCM on several data sets. The experiments demonstrate the efficiency of the proposed algorithm.