Evolving Dynamic Web Pages Using Web Mining

The heterogeneity and the lack of structure that permeates much of the ever expanding information sources on the WWW makes it difficult for the user to properly and efficiently access different web pages. Different users have different needs from the same web page. It is necessary to train the system to understand the needs and demands of the users. In other words there is a need for efficient and proper web mining. In this paper issues and possible ways of training the system and providing high level of organization for semi structured data available on the web is discussed. Web pages can be evolved based on history of query searches, browsing, links traversed and observation of the user behavior like book marking and time spent on viewing. Fuzzy clustering techniques help in grouping natural users and groups, neural networks, association rules and web traversals patterns help in efficient sequential anaysis based on previous searches and queries by the user. In this paper we analyze web server logs using above mentioned techniques to know more about user interactions. Analyzing these web server logs help to closely understand the user behavior and his/her web access pattern