No abstract

A cost-effective parallel VLSI architecture for fuzzy c-means clustering is presented. The architecture reduces the area cost and computational complexity for membership coefficients and centroid computation by employing lookup table based dividers. The usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. Experimental results show that the proposed solution is an effective alternative for cluster analysis with low computational cost and high performance.