Improved user Navigation Pattern Prediction Technique from Web Log Data

AbstractWeb Usage Mining (WUM) is the automatic discovery of user access pattern from web servers. Organizations collect large volumes of data in their daily operations, generated automatically by web servers and collected in server access logs. This paper presents the Prediction of User navigation patterns using Clustering and Classification (PUCC) from web log data. In the first stage PUCC focuses on separating the potential users in web log data, and in the second t stage clustering process is used to group the potential users with similar interest and in the third stage the results of classification and clustering is used to predict the user future requests. The experimental results represent that the approach can improve the quality of clustering for user navigation pattern in web usage mining systems. These results can be use for predicting user's next request in the huge web site