A Fuzzy K-means Clustering Algorithm Using Cluster Center Displacement

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.