Performance analysis of Naïve Bayes, PART and SMO for classification of page interest in web usage mining

User interaction with web sites generates a large amount of web access data stored in the web access logs. Those data can be used for e-commerce to conduct an evaluation of possessed website pages as one of the efforts to understand the desires of the user. Through classification techniques in web usage mining, we conducted an experiment to categorize a number of data obtained from the client log files in two groups namely interest page and un-interest page by using the model page interest estimation. The results obtained indicate that SMO algorithm forms a better classifier models with the result accuracy of 95.8904% and this result is higher when compared with two other algorithms. It can be concluded that the SMO algorithm is efficient in performing classification for this case.