The Application Of WEB Usage Mining Base On Immune And Genetic Algorithm

The tremendous growth in the number and the complexity of information resources and services on the Web has made Web personalization an indispensable tool for both Web-based organizations and for the end users. Web mining technology is one of core techniques used in Web personalization.In recent years there has been an increasing interest and a growing body of work in Web usage mining as an underlying approach to capturing and modeling web user behavioral patterns and for deriving e-business intelligence. Several methods have been developed to extract knowledge from the sessions to describe the navigational behaviors of the users on a Web site. Such patterns can be used to better understand behavioral characteristics of visitors or user segments, improve the organization and structure of the site, and create a personalized experience for visitors by providing dynamic recommendations. One of its popular applications is to find an association rule from database transactions.At first, this paper introduces the importance of Web personalization in E-commerce briefly. Then, the web mining techniques were analyzed and discussed systematically, integrally in this paper, and the progress of association rules discovery in web mining was summarized. And then through studying the genetic algorithm theories, this work proposes a model of Web association rules mining based on genetic algorithm. By researching thoroughly, through analyzing the feasibility of the genetic algorithm and artificial immune system, the advantage and shortcoming of existing in those, it also gives an improving algorithm with immune and genetic technique. Finally, the result of a series of emulate experiment proves that, contrast to traditional method, the Web association rules mining based on immune and genetic algorithm have certain comparative advantage.