Abstract
Data mining deals with the problem of discovering novel and interesting knowledge from large amount of data. It is the core problem in building a fuzzy classification system to extract an optimal group of fuzzy classification rules from fuzzy data set. To efficiently mine the classification rule from databases, a novel classification rule mining algorithm based on particle swarm optimization (PSO) was proposed. The experimental results show that the proposed algorithm achieved higher predictive accuracy and much smaller rule list than other classification algorithm.
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Jiang, Y., Wang, L., Chen, L. (2008). Discovering Interesting Classification Rules with Particle Swarm Algorithm. In: Ishikawa, Y., et al. Advanced Web and Network Technologies, and Applications. APWeb 2008. Lecture Notes in Computer Science, vol 4977. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89376-9_6
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DOI: https://doi.org/10.1007/978-3-540-89376-9_6
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