Title :
On characterization and discovery of minimal unexpected patterns in rule discovery
Author :
Padmanabhan, Balaji ; Tuzhilin, Alexander
Author_Institution :
Oper. & Inf. Manage. Dept., Pennsylvania Univ., Philadelphia, PA, USA
Abstract :
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.
Keywords :
data mining; pattern classification; KDD literature; association rule; data-mining method; minimal unexpected pattern discovery; rule discovery; Association rules; Data mining; Filtering algorithms; Pattern analysis; Performance evaluation; Refining; Testing; Index Terms- Data mining; association rules; minimality.; unexpectedness;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2006.32
