A belief-driven method for discovering unexpected patterns

Several pattern discovery methods proposed in the data mining literature have the drawbacks that they discover too many obvious or irrelevant patterns and that they do not leverage to a full extent valuable prior domain knowledge that decision makers have. In this paper we propose a new method of discovery that addresses these drawbacks. In particular we propose a new method of discovering unexpected patterns that takes into consideration prior background knowledge of decision makers. This prior knowledge constitutes a set of expectations or beliefs about the problem domain. Our proposed method of discovering unexpected patterns uses these beliefs to seed the search for patterns in data that contradict the beliefs. To evaluate the practicality of our approach, we applied our algorithm to consumer purchase data from a major market research company and to web logfile data tracked at an academic Web site and present our findings in the paper. 1