On characterization and discovery of minimal unexpected patterns in rule discovery

• Published in: IEEE transactions on knowledge and data engineering Vol. 18; no. 2; pp. 202 - 216
• Main Authors: Padmanabhan, B., Tuzhilin, A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2006; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Association rules; Computer science; control theory; systems; Data mining; Exact sciences and technology; Filtering algorithms; Index Terms- Data mining; Information systems. Data bases; Marketing; Memory organisation. Data processing; minimality; Pattern analysis; Performance evaluation; Refining; Software; Strength; Testing; unexpectedness; Data analysis; unexpectedness; Association rule; association rules; Information extraction; Data mining; minimality
• ISSN: 1041-4347; 1558-2191
• DOI: 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.