Improving the Cluster Performance by Combining PSO and K-Means Algorithm

Clustering is a technique that can divide data objects into groups based on information found in the data that describes the objects and their relationships. In this paper describe to improving the clustering performance by combine Particle Swarm Optimization (PSO) and K-means algorithm. The PSO algorithm successfully converges during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, K-means algorithm can achieve faster convergence to optimum solution. Unlike K-means method, new algorithm does not require a specific number of clusters given before performing the clustering process and it is able to find the local optimal number of clusters during the clustering process. In each iteration process, the inertia weight was changed based on the current iteration and best fitness. The experimental result shows that better performance of new algorithm by using different data sets.