Swarm Optimisation as a New Tool for Data Mining



This paper proposes the use of Particle Swarm Optimisers
as a tool for data mining. To evaluate its usefulness, we
empirically compare the performance of three variants of
the Particle Optimiser with another evolutionary
algorithm, namely a Genetic Algorithm, in rule discovery
for classification tasks. Such tasks are considered core
tools for Decision Support Systems in a widespread area,
ranging from the industry, commerce, military and
scientific fields. The data sources used here for
experimental testing are commonly used and considered
as a de facto standard for rule discovery algorithms
reliability ranking. The results obtained in these domains
seem to indicate that Particle Swarm Optimisers are
competitive with other evolutionary techniques, and can be
successfully applied to more demanding problem domains.


PSO, Data Mining


Particle Swarm Optimization


IPDPS, April 2003

Cited by

Year 2010 : 1 citations

 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 : 4 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.

 Seyed-Hamid Zahiri, Seyed-Alireza Seyedin (2009). Using Multi-Objective Particle Swarm Optimization for Designing Novel Classifiers. Swarm Intelligence for Multi-objective Problems in Data Mining, Book Series Studies in Computational Intelligence, Volume 242, pp. 65-92, Springer 2009.

 RJ Kuo, CM Chao, YT Chiu (2009). Application of particle swarm optimization to association rule mining, Applied Soft Computing Journal, Elsevier, 2009.

 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.

Year 2008 : 3 citations

 P. Jaganathana, K. Thangavel (2008). A Novel Adaptive Life Cycle Model: Combining Particle Swarm Optimization and Memetic Algorithms. Internatonal Journal of Soft Computing, 3 (4), pp. 297-301, Medweel Journal, 2008.

 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.

 Ching-Shih Tsou, Shih-Chia Chang, Chin-Hsiung Hsu (2008). Particle swarm optimisation with a constraint-handling technique for seller/buyer inventory games. International Journal of Business and Systems Research, Vol 2, nº 2, pp. 214-225, InderScience Publishers, 2008.

Year 2007 : 1 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.

Year 2005 : 1 citations

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