Confidence metrics for association rule mining
- Publication Type:
- Journal Article
- Citation:
- Applied Artificial Intelligence, 2009, 23 (8), pp. 713 - 737
- Issue Date:
- 2009-11-06
Closed Access
Filename | Description | Size | |||
---|---|---|---|---|---|
![]() | 2008007756OK.pdf | 297.65 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
We propose a simple, novel, and yet effective confidence metric for measuring the interestingness of association rules. Distinguishing from existing confidence measures, our metrics really indicate the positively companionate correlations between frequent itemsets. Furthermore, some desired properties are derived for examining the goodness of confidence measures in terms of probabilistic significance. We systematically analyze our metrics and traditional ones, and demonstrate that our new algorithm significantly captures the mainstream properties. Our approach will be useful to many association analysis tasks where one must provide actionable association rules and assist users to make quality decisions.
Please use this identifier to cite or link to this item: