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In this paper, we propose a new method for data mining based on Ant Colony Optimization (ACO). The ACO is a metheuristic inspired by the behavior of real ants, where they search for optimal solutions by considering both local heuristic and previous knowledge, observed by pheromone changes. Good results of the ant colony algorithms for solving combinatorial optimization problems suggest an appropriate effectiveness of the approach also in the task of discovering classification rules in data mining. Mining classification rules is an important research area in data mining. Ant-Miner is an Ant Colony Optimization algorithm for classification task. This paper proposes an improved version of Ant-Miner named Ant-Miner4, which is based on Ant-Miner3 By changing the heuristic function used in the Ant-Miner3, and implementing it based on correction function of Laplace, we tried to redesign Ant-Miner to gain rules with high predictive accuracy. We compared Ant-Miner4 with the previous version (Ant-Miner3) using four data sets. The results indicated that the accuracy of the rules discovered by the new version was higher than the ones gained by the previous version.

A Novel Method For Extracting Classification Rules Based On Ant-miner