The Application And Research Of Web Data Mining

With the development of Internet, Web has been become a large information resource, but the conflict between the unlimited information and lacked of knowledge is notable. Web Usage Mining is a useful method to find user preference and behavior character from Web navigation information, to meet their individual needs. It is important for Web site to operate, manage, carry on e-commerce and attract user. Web usage Mining for users access patterns have three main method: association analysis, clustering analysis and sequential analysis.Firstly, this paper presented the research background and status of Web data mining and rough sets theory. Web log data preprocessing includes four stages of the summarization and study.Combining the granule calculation theory of Rough sets, this paper analyzed and researched Apriori association rules algorithm, and proposed a calculation of supporting the Web pages information granule and a algorithm based on granule calculation of Rough sets for user access sequence association rules mining.According to fuzzy rough theory, clustering and analyze the user access pattern from Web log, and mined the same or similar access pattern for a cluster of users.Based on Web users access pattern and features to analyze user hobbies, and offered personalized services.On this basis, proposed a similarity measurement methods and an approximation of fuzzy clustering method for Web usage mining, and through the examples of analysis, verification efficiency of the algorithm.