Mining Indirect Associations in Web Data

Analysis of association is an important Web mining technique because it can provide useful insight into the navigational behavior of Web users. E-tailers can use this information to develop strategic marketing plans and to re-structure their Web site in order to enhance the browsing experience of their customers . Previous work on mining Web associations has focused primarily on nding frequent access patterns in the data. These patterns can be generated by Web users who share similar information goals or by those with varying interests. Since Web association patterns consider only co-occurrences in data, it is dicult to identify patterns generated by one group of Web users but not by the others. Another drawback of the existing approach is that it does not adequately address the impact of Web site structure on the support of a Web page. As a result, the majority of Web association patterns discovered using conventional techniques contain the home page or other reference pages that have multiple outgoing links. In this study, we apply a new mining technique called indirect association to Web usage data. This novel technique is capable of combining the various association patterns into a more compact structure. It can also capture both positive and negative correlations that exist in the data. We demonstrate the applicability of this technique on Web data from both commercial and research institutions. Our analysis shows very promising results, especially in terms of identifying Web users with distinct interests