Unsupervised Clustering Based an Adaptive Particle Swarm Optimization Algorithm

Clustering analysis is the major application area of data mining where particle swarm optimization (PSO) is being widely implemented due to its simplicity and efficiency. In this paper, we present a new variant of PSO algorithm well tailored to clustering analysis. The proposed algorithm encodes each particle as a bi-dimensional vector, where in the first dimension we look for the optimal number of clusters and in the second dimension, we look for the best centroid of each cluster. In this PSO clustering algorithm a new updating positions rule is proposed to deal with our clustering objective. The performance of the proposed algorithm is tested according to artificial datasets and real datasets. The achieved results present actually good performance and still promising in future perspective.