Knowledge refinement based on the discovery of unexpected patterns in data mining

Abstract

In prior work, we provided methods that generate unexpected patterns with respect to managerial intuition by eliciting managers' beliefs about the domain and using these beliefs to seed the search for unexpected patterns in data. Unexpected patterns discovered in this manner represent contradictions or “holes” in domain knowledge which need to be resolved. Given a belief and a set of unexpected patterns, the motivation behind knowledge refinement is that the belief can be made stronger by refining the belief based on the discovered patterns. In this paper we address the problem of incorporating the discovered contradictions into the belief system based on a formal logic approach. Specifically, we present a framework for refinement based on a generic knowledge refinement strategy, describe abstract properties of refinement algorithms that can be used to compare specific instantiations and then describe and compare two specific refinement algorithms based on this framework.