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<Emphasis Type="Italic">K</Emphasis>-Optimal Rule Discovery |
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| Authors: | |
| Affiliation: | (1) School of Computer Science and Software Engineering, Monash University, P.O. Box 75, Melbourne, Victoria, 3800, Australia;(2) US National Library of Medicine (LHC/CgSB), National Institutes of Health, 8600 Rockville Pike, MS 43 (Bldg 38A, B1N28T), Bethesda, MD 20894, USA |
| Abstract: | K-optimal rule discovery finds the K rules that optimize a user-specified measure of rule value with respect to a set of sample data and user-specified constraints. This approach avoids many limitations of the frequent itemset approach of association rule discovery. This paper presents a scalable algorithm applicable to a wide range of K-optimal rule discovery tasks and demonstrates its efficiency. |
| Keywords: | exploratory rule discovery association rules classification rules rule search search space pruning |
| 本文献已被 SpringerLink 等数据库收录！ | |
