THE USE OF DATA MINING TO PREDICT WEB PERFORMANCE

Abstract

Web mining is the area of data mining that deals with the extraction of interesting knowledge from World Wide Web data. The purpose of this article is to show how data mining may offer a promising strategy for discovering and building knowledge usable in the prediction of Web performance. We introduce a novel Web mining dimension—a Web performance mining that discovers the knowledge about Web performance issues using data mining. The analysis is aimed at the characterization of Web performance as seen by the end users. Our strategy involves discovering knowledge that characterizes Web performance perceived by end users and then making use of this knowledge to guide users in future Web surfing. For that, the predictive model using a two-phase mining procedure is constructed on the basis of the clustering and decision tree techniques. The usefulness of the method for the prediction the future Web performance has been confirmed in a real-world experiment, which showed the average correct prediction ratio of about 80%. The WING (Web pING) measurement infrastructure was used for active measurements and data gathering.