Issue No.02 - February (2006 vol.18)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.32
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.
Balaji Padmanabhan, Alexander Tuzhilin, "On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery", IEEE Transactions on Knowledge & Data Engineering, vol.18, no. 2, pp. 202-216, February 2006, doi:10.1109/TKDE.2006.32