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A drawback of traditional data-mining methods is that they do not leverage prior knowledge of users. In prior work, we proposed a method that could discover unexpected patterns in data by using domain knowledge in a systematic manner. In this paper, we present new methods for discovering a minimal set of unexpected patterns by combining the two independent concepts of minimality and unexpectedness, both of which have been well-studied in the KDD literature. We demonstrate the strengths of this approach experimentally using a case study in a marketing domain.
Index Terms- Data mining, association rules, unexpectedness, minimality.

B. Padmanabhan and A. Tuzhilin, "On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery," in IEEE Transactions on Knowledge & Data Engineering, vol. 18, no. , pp. 202-216, 2006.
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