Abstract
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.
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Roozmand, O., Zamanifar, K. (2008). Parallel Ant Miner 2. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_66
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DOI: https://doi.org/10.1007/978-3-540-69731-2_66
Publisher Name: Springer, Berlin, Heidelberg
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