A Combined Method for Clustering Web Users Based on Evolutionary Algorithms

Web users Clustering involves finding a structure and pattern in a series of web events and leads to personalized pages, recommender systems and direct marketing in e-commerce. It's always important in clustering areas to extract Clusters with the shortest intra-cluster distance and greatest inter-cluster distance. This paper presents an algorithm for clustering web users using a combination of bacteria algorithms and cultural algorithms. In this regard the problem space is modeled using Bacterial Foraging Optimization Algorithm. Then a cultural space is created for the problem that maintains proper norms of spots. The created cultural Space is effective to perform better bacteria evolutionary operations such as gene division and gene integration and prevents accidental operations. Two real data sets, EPA and NASA, are used to tests that. The results indicate better performance of this method compared with other algorithms.