IJARCCE - Computer and Communication Engineering

Abstract

1 Abstract: Data clustering is a popular approach for automatically finding set of objects into a specific number of clusters. Clustering is largely used in many fields including text mining, information retrieval and pattern grouping. Particle Swarm Optimization (PSO) is a population-based optimization algorithm modelled after the simulation of social behaviour of bird flocks and widely used for optimize problem solving. In clustering problem PSO gives optimal solution but takes long time (so called iterations) to find the optimum solution. The hybrid PSO and K-means algorithm is developed to automatically detect the cluster centers of geometrical structure data sets. The proposed algorithm gives the benefits for each of two-merged algorithms. K-means is fast algorithm, PSO optimize the solution. The implementation of the hybrid K-means PSO structure is realized in hardware. The clustering based on hybrid K-means PSO architecture is described by different technique for hardware description (i.e. block diagram) and implemented on field programmable gate array (FPGA). Its feasibility is verified by experiments. Results show that the proposed architecture implemented on the FPGA has a good clustering technique especially for testing with color reduction for true color video.