Research And Application Of Ant Colony Algorithm In Web Usage Mining

As the information on website is more and more abundant and the topology of it is more and more complex,"information overloading"and"resource maze"in information service are ubiquitous. From the point of view of a user, different users have different accessing intention and the accessing intention of one user may vary at different times. On the other hand, only if users' accessing needs have been satisfied in time can information service quality be improved, so that administrators can receive more economic benefits. Adaptive web sites emerge as it is required to solve the problems puzzling both users and website administrators. It automatically improves the organization and presentation by learning form visitors' accessing patterns mined from web server logs, with intention to produce more easily navigable websites.Ant Colony Algorithm is a novel simulated evolvement algorithm solving complicated combinatorial optimization problem and its typical feature is swarm intelligence. Ant Colony Algorithm has been applied in many areas due to the advantage in solving combinatorial optimization problem. Therefore, the application of the ant colony algorithm to web usage mining can help to serve users more efficiently through implementing adaptive websites, namely, transforming the knowledge extracted from Web usage data into website intelligence.Acquiring information that how websites are accessed by users is a guarantee of realizing effective performance of adaptive websites. This paper mainly researches on how to analyze web server logs with web mining technology to obtain the information. To study website adaption, this paper presents Web mining algorithm based on intelligent behavior of ant colony, which is transformed into intelligence of websites, in order to provide individual recommendations or different views to different users. The research is mainly focused on the following aspects.(1) Analyzes demands of adaptive website, discusses the user accessing model and different user accessing patterns that can be mined with web mining technology.(2) Analyzes the similarity between ant colony foraging behavior and users viewing page behavior. Based on ant colony algorithm and the adaptive websites technology, presents an approach to establishing an adaptive website, namely, AAWA algorithm.(3) Presents one kind of novel clustering algorithm, ACRSA clustering algorithm,...