Abstract
Particle Swarm Optimization (PSO) has been extensively studied, in recent past, for solving various engineering optimization problems. There have been many variants of PSO available in literatures. This paper presents a comparative analysis of few popular variants of PSO on the problem of data clustering. The investigated algorithms are evaluated on many real world datasets and few artificial datasets and clustering results are presented. Further, the results of statistical test on effectiveness of each investigated variants of PSO also demonstrated. The convergence characteristics of each variant are shown for different datasets. This study may be helpful to many researchers in choosing suitable PSO variants for their application.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Academic Press (2006)
Jain, A.K., Murthy, M.N., Flynn, P.J.: Data Clustering: a review. Computing Survey, 264–323 (1999)
Jain, A.K.: Data Clustering: 50 years beyond K-means. Pattern Recognition Letters 31, 651–666 (2010)
Castillo, O., Martinez-Marroquin, E., Melin, P., Valdez, F., Soria, J.: Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot. Information Sciences 192, 19–38 (2012)
Kang, F., Li, J., Ma, Z.: Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Information Sciences 181, 3508–3531 (2011)
Kundu, D., Suresh, K., et al.: Multi-objective optimization with artificial weed colonies. Information Sciences 181, 2441–2454 (2011)
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. 6th Int. Symp. Micromachine Human Sci., Nagoya, Japan, pp. 39–43 (1995)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Int. Conf. Neural Networks, pp. 1942–1948 (1995)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proc. IEEE Congr. Evol. Comput. pp. 69–73 (1998)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proc. IEEE Congr. Evol. Comput., Honolulu, HI, pp. 1671–1676 (2002)
Parsopoulos, K.E., Vrahatis, M.N.: UPSO—A unified particle swarm optimization scheme. Lecture Series on Computational Sciences, pp. 868–873 (2004)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: Simpler, maybe better. IEEE Trans. Evol. Comput. 8, 204–210 (2004)
Peram, T., Veeramachaneni, K., Mohan, C.K.: Fitness-distance-ratio based particle swarm optimization. In: Proc. Swarm Intelligence Symp., pp. 174–181 (2003)
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive Learning Particle Swarm optimizer for Global Optimization of Multimodal Functions. IEEE Transactions on Evolutionary Computation 10(3) (2006)
Mertz, C.J., Blake, C.L.: UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/~mlearn/MLRepository.html
Flury, B.: A First Course in Multivariate Statistics, vol. 28. Springer, Berlin (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Naik, A., Satapathy, S.C., Parvathi, K. (2013). A Comparative Analysis of Results of Data Clustering with Variants of Particle Swarm Optimization. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-03756-1_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03755-4
Online ISBN: 978-3-319-03756-1
eBook Packages: Computer ScienceComputer Science (R0)