Fast Fuzzy C-Means Clustering Based on Low-Cost High-Performance VLSI Architecture in Reconfigurable Hardware

Abstract

This paper presents a novel low-cost and high-performance VLSI architecture for fuzzy c-means clustering. In the architecture, the operations at both the centroid and data levels are pipelined to attain high computational speed while consuming low hardware resources. In addition, 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.