Rough Set Based Feature Selection for Web Usage Mining

Web usage mining exploits data mining techniques to discover valuable information from navigation behavior of World Wide Web (WWW) users. The required information is captured by Web servers and stored in Web usage data logs. The first phase of Web usage mining is the pre processing phase. In the preprocessing phase, first, relevant information is filtered from the logs. Data preprocessing is a critical step in Web usage mining. The results of data preprocessing is relevant to the next steps, such as transaction identification, path analysis, association rule mining, sequential pattern mining, and so forth. Feature selection is a preprocessing step in data mining, and it is very effective in reducing dimensions, reducing the irrelevant data, increasing the learning accuracy and improving comprehensiveness. This paper proposes a novel approach for feature selection based on rough set theory for Web usage mining.