Analyzing the User Navigation Pattern from Web Logs Using Maximum Frequent Pattern Approach

Web Usage Mining is the application, it automatically discovers the user access pattern from web servers i.e. logs. The role of the organization is to collect the data from a web server on daily basis. This research work helps to predict the user navigation pattern using the classification and clustering technique. In this first stage, this technique identifies the interested potential users from a weblog. In the second stage, the classification technique is applied to classify users having maximum similar interest. In the third stage, the clustering techniques utilize a classifier to identify and predict the user request based on classification. This experimental result will improve the navigation pattern based on user behavior. This result will be used for predicting the user request on web sites.