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.

Keywords*:* Clustering, K-means, Color image reduction, Particle Swarm Optimization (PSO), field
programmable gate array (FPGA), and Real Time Video Color
Reduction.