The Research On Web-based Usage Mining

The World Wide Web (WWW) continues to grow at an astounding rate in both the sheer volume of traffic and the size and complexity of Web sites. The complexity of tasks suck as Web site design, Web server design, and of simply navigating through a Web site have increased along with this growth. An important input to these design tasks is the analysis of how a Web site is being used. Usage analysis includes straightforward statistics, such as page access frequency, as well as more sophisticated forms of analysis, such as finding the common traversal paths through a Web site. Web Usage Mining is the application of data mining techniques to usage logs of large Web data repositories in order to produce results that can be used in the design tasks mentioned above. However, there are several preprocessing tasks that must be performed prior to applying data mining algorithms to the data collected from server logs. This paper presents several data preparation techniques in order to identify unique users and user sessions. Also, some of data mining algorithms that are commonly used in Web Usage Mining are clustering, association rule generation, sequential pattern generation etc. Clustering analysis allows one to group together users or data items that have similar characteristics. Clustering of user information or data from Web server logs can facilitate the development and execution of future marketing strategies. Association Rule mining techniques discover unordered correlations between items found in a database of transactions. At last, the paper presents a new application example which uses Clustering and Association Rule algorithms to implement personal server.