Identifying Interesting Missing Patterns

One of the important issues in data mining is the subjective “interestingness” problem. It has been shown that in many situations a huge number of patterns can be discovered from a database. Most of these patterns are actually useless or uninteresting to the user. But because of the huge number of patterns, it is difficult for the user to identify those patterns that are of interest to him/her. Past research proposed two main measures of subjective interestingness: unexpectedness and actionability. Both these measures focus on helping the user identify interesting discovered patterns. In this paper, we show that missing patterns (absence of some patterns) are interesting too. An approach has been proposed to identify the interesting missing patterns. The proposed approach is an extension of our previous work. In our previous work, we studied the subjective interestingness problem based on the concept of user’s expectations and fuzzy set theory. In that study, the discovered patterns are ranked in different ways according to their unexpectedness to the user. In this paper, we examine the extension to our previous work so as to identify the interesting missing patterns.