Identifying User Task Using Associative Classification

The explosive use of the web has lead to a massive increase of web sites and boosting the web mining technology. Analyzing and discovering the useful information from the data collected through web is the motive of web mining technology; which also incorporates identification of web user's task. If we have any procedure to find out user task on the basis of their behavior then we can increase the usability of the web. This paper is providing a new approach to identify the user task at the time of navigation through click stream. Using the web logs we have prepared data comprising different browsing events performed by the user. The prepared data has been applied to the new classification approach known as associative classifier; integrating classification and association rule mining. Our paper presents the performance of the CBA (Classification based on association) algorithm for identification of task performed by the web user. In this paper, we have also presented the number of rules generated with the variant support and confidence.