Discovery of Interesting Usage Patterns from Web Data

Abstract

Web Usage Mining is the approach of applying data mining techniques to large Web data repositories in order to extract usage patterns. As with many data mining application domains, the identification of patterns that are considered interesting is a problem that must be solved in addition to simply generating them. A necessary step in identifying interesting results is quantifying what is considered uninteresting in order to form a basis for comparison. Several research efforts have relied on manually generated sets of uninteresting rules. However, manual generation of a comprehensive set of evidence about beliefs for a particular domain is impractical in many cases. Generally, domain knowledge can be used to automatically create evidence for or against a set of beliefs. This paper develops a quantitative model based on support logic for determining the interestingness of discovered patterns. For Web Usage Mining, there are three types of domain information available; usage, content, and structure. This paper also describes algorithms for using these three types of information to automatically identify interesting knowledge. These algorithms have been incorporated into the Web Site Information Filter (WebSIFT) system and examples of interesting frequent itemsets automatically discovered from real Web data are presented.