Efficient mining of association rules using closed itemset lattices
Information Systems
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rule Induction in Cascade Model Based on Sum of Squares Decomposition
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Detection of Local Interactions in the Cascade Model
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Datascape Survey Using the Cascade Model
DS '02 Proceedings of the 5th International Conference on Discovery Science
A Note on Covariances for Categorical Data
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Spiral mining using attributes from 3d molecular structures
AM'03 Proceedings of the Second international conference on Active Mining
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The huge number of descriptive features is often a problem in data mining. We analyzed structure activity data for dopamine antagonists, which involves selecting useful features from numerous fragments extracted from their chemical structures. Correlation coefficients among categorical variables were used to select attributes. Chemists evaluated the rules obtained by the cascade model, and the importance of attribute selection was confirmed.