Applying Web Usage Mining for the Analysis of Web Log Files

Abstract

Today, size of data has reached amazing amounts. Recent advances in technology collecting data in many different sectors is getting easier. At this point, data mining has accelerated the process of transforming data to information. In the beginning, data mining has been known as information extraction from databases, but recently it is more useful for prediction by the help of new methods and technologies developed. In this study web usage mining will be performed with classification methods of data mining using web log files. The data used is an e-commerce web site's log files of 812 days. Web log files contain unstructured data and it is very difficult to analyze it in conventional ways. Before analyzing the data, it has to be cleaned and this process takes long time. The aim of this study is finding the way of purchase behavior. First, analysis is made by support vector machines, then results are compared with the results obtained by logistic regression. For implementation to an e-commerce web site, it can be stated that support vector machines can classify more accurately.