This paper investigates the use of Particle Swarm Optimization (PSO) for cluster analysis. In clustering, primitive exploration with minimum prior knowledge consists of research across a wide variety of communities. The diversity in the field of PSO-based clustering equips us with many tools. This paper provides an integrated framework of the diversified PSO-based clustering. In addition, a novel Particle Swarm Optimization (PSO) algorithm using the concept of aging of particles like bird within a flock is provided. The effectiveness of this concept is demonstrated by cluster analysis. Results show that the model provides enhanced performance and maintains more diversity in the swarm and thereby allows the particles to be robust to trace the changing environment.

The Particle Swarm Optimization (PSO) technique is a population-based stochastic search method. It is based on the concept of the social behavior of a bird flock and their intelligence (Kennedy et al., 1995). In PSO, the population dynamics simulate the behavior of a "birds flock", where social sharing of information takes place and particles profit from the discoveries and previous experience of all other companions during the search for solutions. A swarm in PSO refers to a number of potential solutions to the search/optimization problem, where each potential solution is referred to as a particle. The aim of the PSO is to find the particle position that results in the best evaluation of a given fitness (objective) function.

Each particle represents a point in an n-dimensional space and is flown through this hyper dimensional search space by adjusting the positions towards both the particles' best position (pbest) and the best position in the neighborhood of that particle (gbest). Each particle `i' drives itself by using the following equation:

An Empirical Study of Particle Swarm Optimization for Cluster Analysis,Optimization, particle, clustering, particles, behavior, potential, PSObased, diversified, social, drives, diversified, environment, effectiveness, exploration, fitness, framework, information, intelligence, investigates, maintains, neighborhood, changing, population, performance, algorithm, populationbased, companions, primitive, allows, represents