Web Data Mining Based On The Association Rules Discovery

With the development of computer technology and the popularization of Internet,the WWW data stored in the server expand fast. Although the traditional database and data mining have been developed well and their theory and technology is full-blown,the WWW and application on web have their own characters so that the traditional technology cannot apply to the information mining on WWW directly. The Web log contains the visit information of all users,especially the path information. The analysis of this kind of information is useful for Website designer to know the users' trendency and custom. The designer can use the results of analysis to optimize the structure of website and reorganize the structure of webpage.Traditional association rule techniques arm to mine some relations between transaction items from databases consisting of a set of transaction records. In this work,we try to introduce the notion of association rule into the web mining system and represent the user traversal path hi the form of association rule. The aim is to discover the visit pattern from web log.Web mining categories and study statues are introduced at first,since inaccurary of server log and the huge amount of data,the problems involved in Web usage mining are:how to preprocess the raw weblog to provide an accurate picture of how a site is being used,how to design efficient data mining algorithms to put out rules and patterns. Focusing on these problems,we do research and summarize the preprocessing technology,then take adavantage of the idea of the Apriori algorithms and discuss similar Apriori algorithm which is suitable for discovering user access pattern. In finally,we apply them into practice.