Abstract
Classification rule mining is an important function of data mining, and is applied in many data analysis tasks. The classification rule mining algorithm based-on ant colony optimization (ACO) is researched in this paper. Some improvements are implemented based on existing research to enhance classification predictive accuracy and simplicity of rules. Multi-population parallel strategy is proposed, the cost-based discretization method is adopted, and parameters in the algorithm are adjusted step by step. With these improvements, performance of the algorithm is advanced, and classification predictive accuracy is enhanced. Finally, SIMiner, a self-development data mining software system based on swarm intelligence, is applied to experiment on six data sets taken from UCI Repository on Machine Learning. The results illuminate the algorithm proposed in this paper has better performance in predictive accuracy and simplicity of rules.
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Jin, P., Zhu, Y., Hu, K., Li, S. (2006). Classification Rule Mining Based on Ant Colony Optimization Algorithm. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Control and Automation. Lecture Notes in Control and Information Sciences, vol 344. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37256-1_82
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DOI: https://doi.org/10.1007/978-3-540-37256-1_82
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