Web Usage Mining Based on WAN Users´ Behaviors

Web mining focuses on extracting useful information from large volumes of Web data. Web usage mining (WUM) is one of important application which applies Web mining techniques to discovery usage patterns from Web accessing data. Meanwhile clustering performs a key role in distinguishing different kinds of usage patterns from raw data. Considering usage features of activities, information scope and preference, we propose a two-step K-means clustering algorithm to search user groups in realistic data collected from WAN. In the paper, some useful practical conclusions are also presented to facilitate design of targeting and recommending applications.