An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Genetic Network Programming with Acquisition Mechanisms of Association Rules in Dense Database
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Association rule mining: models and algorithms
Association rule mining: models and algorithms
A study of evolutionary multiagent models based on symbiosis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Relative Linkage Disequilibrium: A New Measure for Association Rules
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Genetic network programming for fuzzy association rule-based classification
CEC'09 Proceedings of the Eleventh conference on Congress on 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
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A method of association rule mining using Alternate Genetic Network Programming (aGNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. aGNP is an extended GNP in terms of including two kinds of sets of node functions. The proposed system can extract important association rules whose antecedent and consequent are composed of the attributes of each family defined by users. The method measures the significance of association via chi-squared test using GNP's features. Rule extraction is done without identifying frequent itemsets used in Apriori-like methods. Therefore, the method can be applied to rule extraction from dense database, and can extract dependent pairs of the sets of attributes in the database. Extracted rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.