Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
A new version of the ant-miner algorithm discovering unordered rule sets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
A survey on parallel ant colony optimization
Applied Soft Computing
Parallel multi-objective Ant Programming for classification using GPUs
Journal of Parallel and Distributed Computing
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In this paper, we propose a flexible parallel ant colony algorithm for classification rule discovery in the large databases. We call this algorithm Parallel Ant-Miner2. This model relies on the extension of real behavior of ants and data mining concepts. The artificial ants are firstly generated and separated into several groups. Each group is assigned a class label which is the consequent parts of the rules it should discover. Ants try to discover rules in parallel and then communicate with each other to update the pheromones in different paths. The communication methods help ants not to gather irrelevant terms of the rule. The parallel executions of ants reduce the speed of convergence and consequently make it possible to extract more new high quality rules by exploring all search space. Our experimental results show that the proposed model is more accurate than the other versions of Ant-Miner.