Classification of Web Log Data to Identify Interested Users Using Naïve Bayesian Classification

Web Usage Mining (WUM) is the process of extracting knowledge from Web user’s access data by exploiting Data Mining technologies. It can be used for different purposes such as personalization, system improvement and site modification. Study of interested web users, provides valuable information for web designer to quickly respond to their individual needs. The main objective of this paper is to study the behavior of the interested users instead of spending time in overall behavior. The existing model used enhanced version of decision tree algorithm C4.5. In this paper, we propose to use the Naive Bayesian Classification algorithm for classifying the interested users and also we present a comparison study of using enhanced version of decision tree algorithm C4.5 and Naive Bayesian Classification algorithm for identifying interested users. The performance of this algorithm is measured for web log data with session based timing, page visits, repeated user profiling, and page depth to the site length. Experimental results conducted shows that the performance metric i.e., time taken and memory to classify the web log files are more efficient when compared to existing C4.5 algorithm.