Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
A Lazy Approach to Associative Classification
IEEE Transactions on Knowledge and Data Engineering
A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A study of evolutionary multiagent models based on symbiosis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving associative classifier for incomplete database using genetic network programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
An evolving associative classifier for incomplete database
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
An evolutionary method for associative local distribution rule mining
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
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A method of association rule mining from incomplete databases is proposed using Genetic Network Programming (GNP). GNP is one of the evolutionary optimization techniques, which uses the directed graph structure. An incomplete database includes missing data in some tuples. Previous rule mining approaches cannot handle incomplete data directly. The proposed method can extract rules directly from incomplete data without generating frequent itemsets used in conventional approaches. In this paper, the proposed method is combined with difference rule mining using GNP for flexible association analysis. We have evaluated the performances of the rule extraction from incomplete medical datasets generated by random missing values. In addition, artificial missing values for privacy hiding are considered using the proposed method.