A hybrid sequential approach for data clustering using K-Means and particle swarm optimization algorithm

Clustering is a widely used technique of finding interesting patterns residing in the dataset that are not obviously known. The K-Means algorithm is the most commonly used partitioned clustering algorithm because it can be easily implemented and is the most efficient in terms of the execution time. However, due to its sensitiveness to initial partition it can only generate a local optimal solution. Particle Swarm Optimization (PSO) technique offers a globalized search methodology but suffers from slow convergence near optimal solution. In this paper, we present a new Hybrid Sequential clustering approach, which uses PSO in sequence with K-Means algorithm for data clustering. The proposed approach overcomes drawbacks of both algorithms, improves clustering and avoids being trapped in a local optimal solution. Experiments on four kinds of data sets have been conducted. The obtained results are compared with K-Means, PSO, Hybrid, K-Means+Genetic Algorithm and it has been found that the proposed algorithm generates more accurate, robust and better clustering results.