Keywords: vector quantization, fuzzy k-means clustering, data clustering, knowledge discovery, pattern recognition
Received September 14, 2009; revised January 19 & March 24, 2010; accepted September 7, 2010.
Communicated by Chih-Jen Lin.
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CHIH-TANG CHANG1, JIM Z. C. LAI2 AND MU-DER JENG1
1Department of Electrical Engineering
2Department of Computer Science and Engineering
National Taiwan Ocean University
Keelung, 202 Taiwan
In this paper, we present a fuzzy k-means clustering algorithm using the cluster center
displacement between successive iterative processes to reduce the computational
complexity of conventional fuzzy k-means clustering algorithm. The proposed method,
referred to as CDFKM, first classifies cluster centers into active and stable groups. Our
method skips the distance calculations for stable clusters in the iterative process. To
speed up the convergence of CDFKM, we also present an algorithm to determine the initial
cluster centers for CDFKM. Compared to the conventional fuzzy k-means clustering
algorithm, our proposed method can reduce computing time by a factor of 3.2 to 6.5 using
the data sets generated from the Gauss Markov sequence. Our algorithm can reduce
the number of distance calculations of conventional fuzzy k-means clustering algorithm
by 38.9% to 86.5% using the same data sets.
Received September 14, 2009; revised January 19 & March 24, 2010; accepted September 7, 2010.
Communicated by Chih-Jen Lin.