  Data clustering is a recognized data analysis method in data mining whereas
K-Means is the well known partitional clustering method, possessing pleasant
features. We observed that, K-Means and other partitional clustering techniques
suffer from several limitations such as initial cluster centre selection,
preknowledge of number of clusters, dead unit problem, multiple cluster
membership and premature convergence to local optima. Several optimization
methods are proposed in the literature in order to solve clustering
limitations, but Swarm Intelligence (SI) has achieved its remarkable position
in the concerned area. Particle Swarm Optimization (PSO) is the most popular SI
technique and one of the favorite areas of researchers. In this paper, we
present a brief overview of PSO and applicability of its variants to solve
clustering challenges. Also, we propose an advanced PSO algorithm named as
Subtractive Clustering based Boundary Restricted Adaptive Particle Swarm
Optimization (SC-BR-APSO) algorithm for clustering multidimensional data. For
comparison purpose, we have studied and analyzed various algorithms such as
K-Means, PSO, K-Means-PSO, Hybrid Subtractive + PSO, BRAPSO, and proposed
algorithm on nine different datasets. The motivation behind proposing
SC-BR-APSO algorithm is to deal with multidimensional data clustering, with
minimum error rate and maximum convergence rate.
