Intelligent Extended Clustering Genetic Algorithm

Abstract

In this paper, the problem of clustering intelligent web using K-means algorithm has been analyzed and the need for a new data clustering algorithm such as Genetic Algorithm (GA) is justified. We propose an Intelligent Extended Clustering Genetic Algorithm (IECGA) using Business Process Execution Language (BPEL) to be an optimal solution for data clustering. It improves the efficiency and performance for retrieving a proper information results that satisfy user's needs. The proposed IECGA uses several mutation operators simultaneously to produce next generation. This series of random mutation process depend on chromosome best fitness in the population and rely on high relevancy as well. The mutation operation will guarantee the success of IECGA for data clustering since it expands the search. So the highly effective mutation operators the greater effects on the genetic process. Finally, IECGA for data clustering gives the user needed documents based on similarity between query matching and relevant document mechanism. The results obtained from the web intelligent search engine are optimal.