Data Clustering by Particle Swarm Optimization with the Focal Particles

Abstract

Clustering is an important technique in data mining. In unsupervised clustering, data is divided into several subsets (clusters) without any prior knowledge. Heuristic optimization based clustering algorithms tries to minimize an objective function, generally a clustering validity index, in the search space defined by the dimensions of the data vectors. If the number of the attributes of the data is large, then this will decrease the clustering performance. This study presents a new clustering algorithm, particle swarm optimization with the focal particles (PSOFP). Contrary to the standard particle swarm optimization (PSO) approach, this new clustering technique ensures high quality clustering results without increasing the dimensions of the search space. This new clustering technique handles communication among the particles in a swarm by using multiple focal particles. The number of focal particles equals to the number of clusters. This approach simplifies the candidate solution representation by a particle and therefore reduces the effect of ‘curse of dimensionality’. Performance of the proposed method on the clustering analysis is benchmarked against K-means, K-means++, hybrid PSO and the CLARANS algorithms on five datasets. Experimental results show that the proposed algorithm has an acceptable efficiency and robustness and superior to the benchmark algorithms.