SOM+PSO. A Novel Method to Obtain Classification Rules.
Keywords:adaptive strategies, self-organizing maps, particle swarm optimization, data mining, classification rules
Currently, most processes have a volume of historical information that makes its manual processing difficult. Data mining, one of the most significant stages in the Knowledge Discovery in Databases (KDD) process, has a set of techniques capable of modeling and summarizing these historical data, making it easier to understand them and helping the decision making process in future situations. This article presents a new data mining adaptive technique called SOM+PSO that can build, from the available information, a reduced set of simple classification rules from which the most significant relations between the features recorded can be derived. These rules operate both on numeric and nominal attributes, and they are built by combining a variation of a population metaheuristic and a competitive neural network. The method proposed was compared with the PART method and measured over 19 databases (mostly from the UCI repository), and satisfactory results were obtained.
 T. Scheffer, “Finding association rules that trade support optimally against confidence,” in Principles o f Data Mining and Knowledge Discovery, ser. Lecture Notes in Computer Science, L. Raedt and A. Siebes, Eds. Springer Berlin Heidelberg, 2001, vol. 2168, pp. 424-435.
 Y. Ye and C.-C. Chiang, “A parallel apriori algorithm for frequent itemsets mining,” in Proceedings o f the Fourth International Conference on Software Engineering Research, Management and Applications, ser. SERA ’06. Washington, DC, USA: IEEE Computer Society, 2006, pp. 87-94.
 J. R. Quinlan, C4.5: programs fo r machine learning. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1993.
 E. Frank and I. H. Witten, “Generating accurate rule sets without global optimization,” in Proceedings o f the Fifteenth International Conference on Machine Learning, ser. ICML '98. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1998, pp. 144-151.
 Z. Wang, X. Sun, and D. Zhang, “A pso-based classification rule mining algorithm,” in Proceedings o f the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects o f Artificial Intelligence, ser. ICIC ’07. Berlin, Heidelberg: Springer-Verlag, 2007, pp. 377-384.
 T. Sousa, A. Silva, and A. Neves, “Particle swarm based data mining algorithms for classification tasks,” Parallel Comput., vol. 30, no. 5-6, pp. 767-783, May 2004.
 N. Khan, M. Iqbal, and A. Baig, “Data mining by discrete pso using natural encoding,” in Future Information Technology (FutureTech), 2010 5th International Conference on, 2010, pp. 1- 6.
 N. Khan, A. Baig, and M. Iqbal, “A new discrete pso for data classification,” in Information Science and Applications (ICISA), 2010 International Conference on, 2010, pp. 1-6.
 M. Chen and S. Ludwig, “Discrete particle swarm optimization with local search strategy for rule classification,” in Nature and Biologically Inspired Computing (NaBIC), 2012 Fourth World Congress on, 2012, pp. 162-167.
 Y. Jiang, L. Wang, and L. Chen, “A hybrid dynamical evolutionary algorithm for classification rule discovery,” in Intelligent Information Technology Application, 2008. IITA ’08. Second International Symposium on, vol. 3, 2008, pp. 76-79.
 H. Wang and Y. Zhang, “Improvement of discrete particle swarm classification system,” in Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on, vol. 2, 2011, pp. 1027-1031.
 L. Yan and J. Zeng, “Using particle swarm optimization and genetic programming to evolve classification rules,” in Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on, vol. 1, 2006, pp. 3415-3419.
 A. Ozcift, M. Kaya, A. Gülten, and M. Karabulut, “Swarm optimized organizing map (swom): A swarm intelligence based optimization of self-organizing map,” Expert Systems with Applications, vol. 36, no. 7, pp. 10640 - 10648, 2009.
 C. Hung and L. Huang, “Extracting rules from optimal clusters of self-organizing maps,” in Computer Modeling and Simulation, 2010. ICCMS ’10. Second International Conference on, vol. 1, 2010, pp. 382-386.
 H. W. and L. L., “Dynamic self-organizing maps,” in XXXI Conf. Latinoamericana de Informatica, C E LI2005, 2005.
 T. Kohonen, “Neurocomputing: foundations of research,” J. A. Anderson and E. Rosenfeld, Eds. Cambridge, MA, USA: MIT Press, 1988, ch. Self-organized formation of topologically correct feature maps, pp. 509-521.
 J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. o f the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, Eds., vol. 1. University of California Press, 1967, pp. 281-297.
 T. Kohonen, M. R. Schroeder, and T. S. Huang, Eds., Self-Organizing Maps, 3rd ed. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2001.
 J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceedings o f the IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.
 ------ , “A discrete binary version of the particle swarm algorithm,” in Proceedings o f the IEEE International Conference on Systems, Man, and Cybernetics, vol. 5. Washington, DC, USA: IEEE Computer Society, 1997, pp. 4104-4108.
 L. Lanzarini, J. Lopez, J. A. Maulini, and A. Giusti, “A new binary pso with velocity control,” in Advances in Swarm Intelligence, ser. Lecture Notes in Computer Science, Y. Tan, Y. Shi, Y. Chai, and G. Wang, Eds. Springer Berlin Heidelberg, 2011, vol. 6728, pp. 111-119.
 G. Venturini, “Sia: A supervised inductive algorithm with genetic search for learning attributes based concepts,” in Machine Learning: ECML-93, ser. Lecture Notes in Computer Science, P. Brazdil, Ed. Springer Berlin Heidelberg, 1993, vol. 667, pp. 280-296.
 Y. Shi and R. Eberhart, “Parameter selection in particle swarm optimization,” in Evolutionary Programming VII, ser. Lecture Notes in Computer Science, V. Porto, N. Saravanan, D. Waagen, and A. Eiben, Eds. Springer Berlin Heidelberg, 1998, vol. 1447, pp. 591-600.
 J. Kennedy and R. C. Eberhart, Swarm intelligence. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2001.
 K. Bache and M. Lichman, “UCI machine learning repository,” 2013. [Online]. Available: http://archive.ics.uci.edu/ml