Published Paper Details:

Data mining is the non-trivial extraction of novel, intrinsic, and hidden predictive knowledge from large data sets is an evolving new technology which is a direct result of the increasing use of computer databases in order to store and retrieve information effectively. This paper gives an idea of optimization algorithm by which the efficient result can be fetched. Optimization is a dire need for a large amount of data processing. So that optimization is a challenging issue in data mining. It seems to be that there are many different approaches has been proposed by authors in order to optimize the results. Particle swarm optimization (PSO) is an evolutionary computation technique. Proposed methodology is the iterative process of PSO algorithm, inertia weight adjustment is usually expected to make particles have stronger global searching capability in early stage to prevent premature convergence and have stronger local search capability in latter stage to accelerate convergent speed. In other words, the inertia weight should vary nonlinearly along with the process of decreasing slowly, then rapid and then slowly again so as to attain fast convergence speed in prophase and have local search capability to a certain degree at the later stage, too. In this paper PSO based k-means clustering is applied to generate clusters. And provide multimodal and higher dimensional complicated optimization problems, and can accelerate convergence speed, improve optimization quality effectively in comparison to the algorithms of K-means.