Enhance Clustering Approach using PSO-A* for E-Commerce

Abstract

In the current trends most of the people uses online searching and purchasing the items. So web data size is drastically increases and after some time not possible to put all data in memory. So there is a need to manage data access speed and also increase the speed of searching. In the existing system most of clustering algorithm uses only basic criteria for machine learning and data mining algorithms. Initially it is not able to select the good center point so it will do to increase the number of iteration. Second problem is to access of full database and third problem is needed to increase searching speed. The proposed approach uses PSO-A* algorithm for clustering and searching that is used for select the good center point initial for clustering. After that only clustered data is access not full database that make partial database access to manage memory management and speed. Finally third approach is uses for increase searching speed. Now the proposed approach is to perform well and improve the ecommerce website performance.

A survey of the state-of-the-art swarm intelligence techniques and their application to an inverse design problem

“…PSO has a propensity to trapped in local minima resulted in a premature convergence and weak exploitation in the final stage. Over the years, PSO has been used in many areas these include in the field of Communication [91][92][93], Robotics [94][95][96], Image processing [97][98][99], Electrical [100][101][102], Management [103,104], and many others.…”