Abstract
The Ant-Miner algorithm, Ant-Miner2, Ant-Miner3 and Taco-Miner have an excellent performance in classification tasks, what can be seen in literature. These algorithms are inspired on the behavior of real ant colonies and some data mining concepts as well as principles. This paper presents a new algorithm based on Ant Colony whose experiments comparing with the others suggest superiority.
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Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: New Ideas in Optimization, pp. 11–32. McGraw Hill, London (1999)
Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(2), 137–172 (1999)
Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data Mining with an Ant Colony Optimization Algorithm. IEEE Transactions on Evolutionary Computing 6(4) (2002)
Rozin, V., Margaliot, M.: The Fuzzy Ant. IEEE Computational Intelligence Magazine 2(4) (2007)
Parpinelli, R.S.: Um Algoritmo Baseado em ColĂ´nias de Formigas para ClassificaĂ§Ă£o e, Data Mining. DissertaĂ§Ă£o de Mestrado, UTFPR, Curitiba (2001) (in Portuguese)
Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from database perspective. Proceedings of the IEEE Transactions on Knowledge and Data Engineering, 866–883 (1996)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)
Clark, P., Neblett, T.: The CN2 induction algorithm. Machine Learning 3, 261–283 (1989)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, New York (1991)
Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010), http://archive.ics.uci.edu/ml
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)
Liu, B., Abbass, H.A., Mckay, B.: Density-Based Heuristic for Rule Discovery with Ant- Miner. In: Australia-Japan Workshop on Intelligent and Evolutionary Systems (2002)
Liu, B., Abbass, H.A., Mckay, B.: Classification Rule Discovery with Ant Colony Optimization. In: IAT 2003, International Conference on Intelligent Agent Technology (2003)
Schools, L., Naudts, B.: Ant Colonies are Good at Solving Constraint Satisfaction Problems. In: Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 1190–1195 (2000)
Sun, R., Tatsumi, S., Zhao, G.: Multiagent Reinforcement Learning Method with An Improved Ant Colony Systems. In: Proceedings of the 2001 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 1612–1617 (2001)
Thangavel, K., Jaganathan, P.: Rule Minig Algorithm with a New Ant Colony Optimization Algorithm. In: IEEE International Conference on Computational Intelligence and Multimedia Applications (2007)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: An overview. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery & Data Mining, pp. 1–34. MIT Press, Cambridge (1996)
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Costa Junior, I. (2013). Data Mining with Ant Colony Algorithms. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_4
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DOI: https://doi.org/10.1007/978-3-642-39482-9_4
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