Back to Search
Start Over
On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery.
- Source :
- IEEE Transactions on Knowledge & Data Engineering; Feb2006, Vol. 18 Issue 2, p202-216, 15p
- Publication Year :
- 2006
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 18
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 19623189
- Full Text :
- https://doi.org/10.1109/TKDE.2006.32