Abstract not found

Abstract not found

Web Usage Mining is the application of data mining techniques to large Web data repositories in order to extract usage patterns. As with many data mining application domains, the identification of patterns that are considered interesting is a problem that must be solved in addition to simply generating them. A necessary step in identifying interesting results is quantifying what is considered uninteresting in order to form a basis for comparison. Several research efforts have relied on manually generated sets of uninteresting rules. However, manual generation of a comprehensive set of evidence about beliefs for a particular domain is impractical in many cases. Generally, domain knowledge can be used to automatically create evidence for or against a set of beliefs. For Web Usage Mining, there are three types of domain information available; usage, content, and structure. The Web Site Information Filter (WebSIFT) system uses the content and structure information from a Web site...

Web mining techniques should have the analysis of information originated in the web must be helpful to all users for what they are looking for the personalization of site according to users understanding in website should adopt web data accurately. Only simple context like free text only cannot be so much attracted by all the users. Web site structure have to play major role with special techniques, significant patterns about the preferences and user behaviour to personalize the web site. Many times web site structure seems to be complex, hiding the data which causes “ Lost in Hyperspace”. In this paper let we go through main web mining techniques for the accurate targeted knowledge on the user behaviour in site. It is the combination of hybrid and intelligence techniques for web data mining.