One of the central problems in knowledge discovery is the development of good measures of interestingness of discovered patterns. With such measures, a user needs to manually examine only the more interesting rules, instead of each of a large number of mined rules. Previous proposals of such measures include rule templates, minimal rule cover, actionability, and unexpectedness in the statistical sense or against user beliefs.
In this paper we will introduce neighborhood-based interestingness by considering unexpectedness in terms of neighborhood-based parameters. We first present some novel notions of distance between rules and of neighborhood of rules. The neighborhood-based interestingness of a rule is then defined in terms of the pattern of the fluctuation of confidences or the density of mined rules in some of its neighborhoods. Such interestingness can also be defined for sets of rules (e.g. plateaus and ridges) when their neighborhoods have certain properties. We can rank the interesting rules by combining some neighborhood-based characteristics, the support and confidence of the rules, and users feedback. We discuss how to implement the proposed ideas and compare our work with related ones. We also give a few expected tendencies of changes due to rule structures, which should be taken into account when considering unexpectedness. We concentrate on association rules and briefly discuss generalization to other types of rules.
& Li, J.
(1998). Interestingness of Discovered Association Rules in terms of Neighborhood-Based Unexpectedness. Lecture Notes in Computer Science, 1394, 72-86.