CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
A method of association rule analysis for incomplete database using genetic network programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Heuristic rule-based regression via dynamic reduction to classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
An evolving associative classifier for incomplete database
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
| Hi-index | 0.00 |
A method for rule mining for continuous value prediction has been proposed using a graph structure based evolutionary computation technique. The method extracts the rules named associative local distribution rule whose consequent part has a narrow distribution of continuous value. A set of associative local distribution rules is applied to the continuous value prediction. The experimental results showed that the method can bring us useful rules for the continuous value prediction. In addition, two cases of contrast rules are defined based on the associative local distribution rules. The performances of the contrast rule extraction were evaluated and the results showed that the proposed method has a potential to realize contrast analysis between two datasets.