# Particle Swarm Based Data Mining Algorithms for Classification Tasks

### Authors

### Abstract

Particle Swarm Optimisers are inherently distributed algorithms where the solution for a problem emerges from the interactions between many simple individual agents called particles. This article proposes the use of the Particle Swarm Optimiser as a new tool for Data Mining. In the first phase of our research, three different Particle Swarm Data Mining Algorithms were implemented and tested against a Genetic Algorithm and a Tree Induction Algorithm (J48). From the obtained results, Particle Swarm Optimisers proved to be a suitable candidate for classification tasks. The second phase was dedicated to improving one of the Particle Swarm optimiser variants in terms of attribute type support and temporal complexity. The data sources here used for experimental testing are commonly used and consideredas a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that Particle Swarm Data Mining Algorithms are competitive, not only with other evolutionary techniques, but also with industry standard algorithms such as the J48 algorithm, and can be successfully applied to more demanding problem domains.

### Subject

Particle Swarm Optimization### Journal

Parallel Computing, #30, pp. 767-783, Elsevier B. V., January 2004### Cited by

#### Year 2010 : 3 citations

Hongbo Liu, Ajith Abraham and Benxian Yue (2010). Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction. Advances in Machine Learning II, Studies in Computational Intelligence, Volume 263, pp. 445-466, Springer 2010.

Suraj Pandey, Linlin Wu, Siddeswara Guru, and Rajkumar Buyya (2010). A Particle Swarm Optimization (PSO)-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments, Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications (AINA 2010), Perth, Australia, April 20-23, 2010

Adham Atyabi, Somnuk Phon-Amnuaisuk, Chin Kuan Ho (2010). Applying Area Extension PSO in Robotic Swarm. Journal of Intelligent & Robotic Systems, Volume 58, Numbers 3-4, pp. 253-285, Springer 2010.

#### Year 2009 : 27 citations

A. B. S. Serapião and J. R. P. Mendes (2009). Classification of Petroleum Well Drilling Operations with a Hybrid Particle Swarm/Ant Colony Algorithm. Next-Generation Applied Intelligence, Lecture Notes in Computer Science, 5579, pp. 301-310, Springer 2009.

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Adham Atyabi, Somnuk Phon-Amnuaisuk, Chin Kuan Ho (2009). Applying Area Extension PSO in Robotic Swarm. Journal of Intelligent and Robotic Systems , pp. 1-33, Springer Netherlands, 2009.

Ajith Abraham and Hongbo Liu (2009). Turbulent Particle Swarm Optimization Using Fuzzy Parameter Tuning. Foundations of Computational Intelligence, Studies in Computational Intelligence, Volume 3, pp. 291-312, Springer 2009.

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Hongbo Liu, Ajith Abraham and Yanheng Li (2009). Nature Inspired Population-Based Heuristics for Rough Set Reduction. Rough Set Theory: A True Landmark in Data Analysis, Studies in Computational Intelligence, Volume 174, 261-278, Springer 2009.

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Suraj Pandey, Linlin Wu, Siddeswara Guru, Rajkumar Buyya (2009). A Particle Swarm Optimization (PSO)-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments. NET-based Cloud Computing, Technical Report, CLOUDS-TR-2009.

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#### Year 2008 : 8 citations

Y Zheng, Y Meng (2008), OBJECT DETECTION AND TRACKING USING BAYES-CONSTRAINED PARTICLE SWARM OPTIMIZATION, Computer Vision Research Progress, 2008 - Nova Science Publishers

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Abraham, A. Hongbo Liu (2008). Swarm intelligence based rough set reduction scheme for support vector machines. Proceedings of the IEEE International Conference on Intelligence and Security Informatics, pp. 200-202, IEEE Press, 2008.

Nicholas Holden and Alex A. Freitas (2008). A Hybrid PSO/ACO Algorithm for Discovering Classification Rules in Data Mining. Journal of Artificial Evolution and Applications, Volume 2008, Hindawi Publishing , 2008.

Neveen Ibrahim Ghali, Nahed Eldesouky, Mervat A. Nabyand Lamiaa Bakrawy (2008). IMPROVEMENT OF DATA CLUSTERING USING PARTICLE SWARM OPTIMIZATION, Far East Journal of Electronics and Communications, Volume 2, Issue 2, Pages 121 - 132, 2008.

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#### Year 2007 : 12 citations

De Falco, A Della Cioppa, E Tarantino - Facing classification problems with Particle Swarm Optimization. Applied Soft Computing, 2007 ¨C Elsevier.

H Liu, A Abraham, M Clerc - Chaotic dynamic characteristics in swarm intelligence. Applied Soft Computing, 2007 ¨C Elsevier.

Nicholas Paul Holden, Alex A. Freitas. A hybrid PSO/ACO algorithm for classification. Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, London, United Kingdom. WORKSHOP SESSION: Particle swarms the second decade, pp. 2745-2750, ISBN:978-1-59593-698-1, ACM Press 2007.

H Liu, A Abraham, W Zhang, A fuzzy adaptive turbulent particle swarm optimization, International Journal of Innovative Computing and Applications, Volume 1, Number 1, pp. 39 ¨C 47, 2007.

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Cui Zhi-hua, Zeng Jian-chao, Sun Guo-ji (2007): Adaptive Integral-controller Particle Swarm Optimization Using Accelerator Feedback. Journal of Chinese Computer Systems, vol.28, No.5, pp. 855-860, 2007.

Eduardo P. Costa , Ana C. Lorena , André C. P. L. F. Carvalho , Alex A. Freitas and Nicholas Holden (2007): Comparing Several Approaches for Hierarchical Classification of Proteins with Decision Trees. Advances in Bioinformatics and Computational Biology, LNCS 4643, pp.126-137, Springer, 2007.

Inthachot, Montri Supratid, Siriporn (2007): A Multi-Subpopulation Particle Swarm Optimization: A Hybrid Intelligent Computing for Function Optimization.

In Natural Computation, 2007, vol 5, pp. 679-684, IEEE Press.

Lei, Wang; Qi, Kang; Hui, Xiao; Qidi, Wu, Traffic Intelligent Optimization and Local Traffic-Flow Control Inside Shanghai-EXPO-Area, Networking, Sensing and Control, 2007 IEEE International Conference on. Volume , Issue , 15-17 April 2007 Page(s):856 " 861

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Liang-Chi Wang (2007). Classification Rule Discovery with Particle Swarm Optimization. Master Thesis, 2007.

#### Year 2006 : 8 citations

N Holden, AA Freitas, Hierarchical Classification of G-Protein-Coupled Receptors with a PSO/ACO Algorithm, Proc. IEEE Swarm Intelligence Symposium (SIS-2006), pp. 77-84.

Crina Grosan, Ajith Abraham and Monica Chis: Swarm Intelligence in Data Mining, Studies in Computational Intelligence (SCI) 34, 1"20 (2006).

De Falco, I. Cioppa, A. D. Tarantino, E., Evaluation of Particle Swarm Optimization Effectiveness in Classification, Lecture Notes in Computer Science, 2006, Vol. 3849, pages 164-171., Springer-Verlag.

Cui, Z. Cai, X. Zeng, J. Sun, G., Predicted-Velocity Particle Swarm Optimization Using Game-Theoretic Approach, Lecture Notes in Computer Science, 2006, Vol. 4115, pages 145-154, Springer-Verlag.

Qi Kang, Lei Wang, Qi-di Wu, Research on Fuzzy Adaptive Optimization Strategy of Particle Swarm Algorithm, International Journal of Information Technology, Vol.12, No.3, 2006.

Wang Lei Kang Qi Wu Qi-Di, Multi-optimum Fuzzy Programming Based Particle Swarm Optimization, Control And Decision, 2006 Vol.21 No.6 P.680-684

Liu Hong-Bo Wang Xiu-Kun Tan Guo-Zhen, Convergence Analysis of Particle Swarm Optimization and Its Improved Algorithm Based on Chaos, Control And Decision, 2006 Vol.21 No.6 P.636-640,645

A. Abraham, He Guo, and Hongbo Liu, "Swarm Intelligence: Foundations, Perspectives and Applications", in Swarm Intelligence in Data Mining, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), approx. 300 pages (hardcover), Springer, Germany, 2006.

#### Year 2005 : 5 citations

Nicholas Holden, Alex A. Freitas, A Hybrid Particle Swarm/Ant Colony Algorithm for the Classification of Hierarchical Biological Data, 2005 IEEE Swarm Intelligence Symposium, 8-10 June, Pasadena, California, USA.

Wang Lei, Kang Qi, Xiao Hui, Wu Qidi, "A Modified Adaptive Particle Swarm Optimization Algorithm?, ICIT 2005. IEEE International Conference on Industrial Technology, 14-17 Dec. 2005.

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KANG Qi, WANG Lei, WU Qi-di, "Fuzzy Adaptive Programming Algorithm Based on Particle Swarm Multi-optimum Information?, INFORMATION AND CONTROL, 2005 Vol.34 No.4 P.439-443,450