CISUC - A Particle Swarm Data Miner
CISUC

A Particle Swarm Data Miner

Authors

Abstract

This paper describes the implementation of Data Mining
tasks using Particle Swarm Optimisers. The object of our research has
been to apply such algorithms to classi¯cation rule discovery. Results,
concerning accuracy and speed performance, were empirically compared
with another evolutionary algorithm, namely a Genetic Algorithm and
with J48 - a Java implementation of C4.5. The data sets used for ex-
perimental testing have already been widely used and proven reliable
for testing other Data Mining algorithms. The obtained results seem to
indicate that Particle Swarm Optimisers are competitive with other evo-
lutionary techniques, and could come to be successfully applied to more
demanding problem domains.

Keywords

PSO, Data Mining

Subject

Particle Swarm Optimization

Conference

EPIA\'03, December 2003


Cited by

Year 2009 : 2 citations

 Xingjuan Cai and Ying Tan (2009). A study on the effect of vmax in particle swarm optimisation with high dimension.International Journal of Bio-Inspired Computation, Volume 1, Number 3, pp. 210 - 216, InderScience Publishers 2009.

 Ma Junhua, Lu Yansheng, Dou Quansheng, Jiang Ping (2009). Power Load Forecasting Model Based on Harmonic Clustering and Classification Method. International Conference on Intelligent Computation Technology and Automation, Vol 3, pp. 79-82, IEEE Press, 2009.

Year 2008 : 2 citations

 Yudel Gómez, Rafael Bello, Amilkar Puris, María M. García (2008). Two Step Swarm Intelligence to Solve the Feature Selection Problem. Journal of Universal Computer Science, vol. 14, no. 15, pp. 2582-2596, 208.

 Zhihua Cui, Jianchao Zeng, Yufeng Yin (2008). An Improved PSO with Time-Varying Accelerator Coefficients. Eighth International Conference on Intelligent Systems Design and Applications, Volume 2 pp. 638 - 643, IEEE Press, 2008.

Year 2007 : 3 citations

 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.

 Xingjuan Cai, Zhihua Cui, Jianchao Zeng and Ying Tan (2007). Perceptive Particle Swarm Optimization: A New Learning Method from Birds Seeking
LNCS Volume 4507/2007, Book Computational and Ambient Intelligence pp.1130-1137, Springer 2007.

 Bello, Rafael Gomez, Yudel Nowe, Ann Garcia, Maria M. (2007).
Two-Step Particle Swarm Optimization to Solve the Feature Selection Problem. Intelligent Systems Design and Applications, 2007. ISDA 2007.
pp. 691-696. IEEE Press 2007.

Year 2006 : 3 citations

 Lhotska, L. Macas, M. Bursa, M., PSO and ACO in Optimization Problems, Lecture Notes in Computer Science, 2006, Vol. 4224, pages 1390-1398, Springer-Verlag.

 SHAN Shi-min, DENG Gui-shi, HE Ying-hao, Comparison Studies on Application Research on Knowledge Discovery Using Swarm Intelligence, Application Research of Computers, 1001-3695(2006).

 Hongbo Liun, Bo Li, Ye Ji and Tong Sun, Particle Swarm Optimisation from lbest to gbest, Advances in Soft Computing, Springer , Volume 2006, Applied Soft Computing Technologies: The Challenge of Complexity, pages 537-545.

Year 2005 : 2 citations

 Andries P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John Wiley, 2005.

 Lhotská, L., Maca?, M., Strackeljan, J., Jantzen, J.: Applied Nature Inspired System: Preliminary Investigation. In European Symposium on Nature-inspired Smart Information Systems [CD-ROM], 2005

Year 2004 : 1 citations

 Yu Liu, Zheng Qin, Zhewen Shi, Junying Chen, Rule Discovery with Particle Swarm Optimization, Content Computing: Advanced Workshop on Content Computing, AWCC 2004, ZhenJiang, JiangSu, China, November 15-17, 2004 Lecture Notes in Computer Science, Volume 3309, Jan 2004, Pages 291 - 296