Research Of Fuzzy Clustering Algorithm And Its Application On Web Session

With the rapid development of Internet, especially the global popularization of Web, the information volume of Web becomes rich. We can acquire the information that we need from Web Mining. Analyzing the users' visiting behavior,frequency and content, we can get the general acknowledge of users' visiting behavior and manner, so that we can improve the design of Web. And more importantly, we can develop better electronic business through the comprehension and analysis of user character.Web Log Mining can abstract user visiting pattern and knowledge from web log. It can identify the potential user and heighten the quality of information service and improve the performance of web service system that analyzing and exploring the discipline of the log. The web session clustering has obvious fuzzy character, so sometimes the fuzzy clustering is more suitable for web clustering than traditional clustering.This thesis analyses and discusses the correlative conception of data mining and web log mining. Firstly, the paper pretreat the web log, and it lays a solid foundation for the next pattern excavation; Secondly, the paper introduces the method of clustering analysis and the correlative conception and method of fuzzy clustering; Lastly, the paper applies the clustering method based on fuzzy equivalence relation to web session clustering. The thesis analyzes the existing algorithm for transitive closures. Since the existing algorithm is inefficient and has a large operation quantity, this paper applies the t-bridge algorithm to fuzzy equivalence matrix clustering algorithm. The paper presents the model of the algorithm and the case, at the same time, the paper presents the provment of the algorithm and validate it with experiment.