Research And Application Of Association Rule In Data Mining

With the development of computer technology and the popularization of Internet, Web and Web usage information is becoming the largest information Warehouse with the exploding rise of www. So the conflict between the limited human attention and the unlimited information is notable. Web data mining is a useful method to solve such problem, but the www data 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 the Website designer to know the users Web usage pattern. The designer can use the result of analysis to optimize the structure of Website and reorganize the structure of Webpage.Traditional association rule techniques aim 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 in the form of association rule. The aim is to discover the visit patterns from the Web log. In the paper we have a systematic research into the association rules of data mining. Web mining categories, study content and aim are introduced at first. We propose the methods to get the user path from the Original log data. Then we give two methods to mine the user s access association rule. One is the Maximal Forward References(MF)method, and it is like the traditional data mining methods, Apriori. The other method is regard the hypertext system as a weighted directed graph. After the redefinition of the confidence and support, we propose the composite association rule mining method.