Prediction of Web User Behavior by Discovering Temporal Relational Rules from Web Log Data

Abstract

The Web has become a very popular and interactive medium in our lives. With the rapid development and proliferation of e-commerce and Web-based information systems, web mining has become an essential tool for discovering specific information on the Web. There are a lot of previous web mining techniques have been proposed. In this paper, an approach of temporal interval relational rule mining is applied to discover knowledge from web log data. Comparing our proposed approach and previous web mining techniques, the attribute of timestamp in web log data is considered in our approach. Firstly, temporal intervals of accessing web pages are formed by folding over a periodicity. And then discovery of relational rules is performed based on constraint of these temporal intervals. In the experiment, we analyze the result of relational rules and the effect of important parameters used in the mining approach.