DATA MINING BY ADOPTING PARTICLE SWARM OPTIMIZATION

Abstract

The complexity of many existing data mining algorithms is exponential with respect to the number of dimensions. With increasing dimensionality, these algorithms soon become computationally intractable and therefore inapplicable in many real applications. Secondly, the specificity of similarities between points in a high dimensional space diminishes. Continuous improvement processes based on the principles of total quality management that including customer orientation, quality orientation and affairs implementation as shape of team is always from interest principle of dynamic and successful organizations. PSO is a stochastic, population-based evolutionary algorithm particularly suitable for solving multi- variable optimization problems. It embeds a kind of swarm intelligence that is based on socio-psychological principles and provides insights into social behavior contributing to engineering applications.