Fast discovery of association rules
Advances in knowledge discovery and data mining
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Threshold tuning for improved classification association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Association and classification rule mining are two well-known techniques used in data mining. The integrated approach is known as associative classification rule mining (ACRM), which has helped in developing a compact and efficient classifier for the classification of unknown samples. In this paper, we treated the ACRM as a multi-objective problem and applied the Parallel Multi-objective Genetic Algorithms (PMOGAs) to solve it. ACRM is associated with two phases like rule extraction and rule selection. As ACRM is a multi-objective problem so by applying PMOGA on it we can optimize the measures like support and confidence of association rule mining to extract classification rules in rule extraction phase and in rule selection phase a small number of rules are targeted from the extracted rules to design an accurate and compact classifier, which can maximize the accuracy of the rule set and minimize their complexity. Experiments are conducted on UCI data set by using MOGA and PMOGA. Finally the computational results are analyzed and concluded that the PMOGA for multi-objective rule selection generates a Pareto optimal rule sets with a compact set of classification rules in less time vis-a-vis to MOGA without severely degrading their classification accuracy.