An Advanced Optimal Web Intelligent Model for Mining Web User Usage Behavior using Genetic Algorithm

Abstract

With the continued growth and proliferation of Web services and Web based information systems, the volumes of user data have reached astronomical proportions. Analyzing such data using Web Usage Mining can help to determine the visiting interests or needs of the web user. This type of analysis involves the automatic discovery of meaningful patterns, which represents fine grained navigational behavior of visitors from a large collection of semi structured web log data. Due to the non linear and complex nature of weblog all the existing conventional mining techniques are failed in the process of pattern discovery, result in only local optimal solutions. In order to get the global optimal solutions, Web intelligent models are required such as Genetic algorithms. The present paper introduces the Advanced Optimal Web Intelligent Model with granular computing nature of Genetic Algorithms, IOG. The IOG model is designed on the semantic enhanced content data, which works more efficiently than that on normal data. The unique parameters of the IOG fitness function generates balanced weights, to represent the significance of characteristics, which yields the dissimilar characteristics of page vector. The evaluation function of IOG improves the page quality and reduces the execution time to a specified number of iterations as it considers practical measures like page, link and mean qualities. The genetic operators are intended in drawing the stickiness among the characteristics of a page vector. By integrating all the above the web intelligent model IOG, proceed towards intelligence and significantly improves the investigation of web user usage behavior.