Abstract
Clustering techniques suffer from fact that once they are merged or split, it cannot be undone or refined. Considering the stability of the Genetic Algorithm and the local searching capability of Swarm Optimization in clustering, these two algorithms are combined. Genetic Algorithms, being global search technique, have been widely applied for discovery of clusters. A novel data clustering based on a new optimization scheme which has benefits of high convergence rate and easy implementation method is been proposed were in local minima is disregarded in an intelligent manner. This paper, we intend to apply GA and swarm optimization (i.e., PSO) technique to optimize the clustering. We exemplify our proposed method on real data sets from UCI repository. From experimental results it can be ascertained that combined approach i.e., PSO_GA gives better clustering accuracy compare to PSO-based method.
This is a preview of subscription content, log in via an institution to check access.
References
H. Stahl, “Cluster Analysis of Large Data Sets”, In W. Gaul and M. Schader, editors Classification as a Tool of Research, pp. 423–430, Elsevier, Amsterdam (1986).
Santhosh Peddi, Alok Singh, Grouping Genetic Algorithm for Data Clustering, Springer Berlin Heidelberg, Swarm, Evolutionary, and Memetic Computing, Lecture Notes in Computer Science, Volume 7076, pp. 225–232 (2011).
Jayshree Ghorpade-Aher, Vishakha Arun Metre, PSO based Multidimensional Data Clustering: A Survey, International Journal of Computer Applications (0975–8887), Volume 87, No. 16, (2014).
K. E. Parsopoulos and M. N. Vrahatis, “Recent approaches to global optimization problems through particle swarm optimization,” Natural Computing. An International Journal, vol. 1, no. 2–3, pp. 235–306(2002).
A. Sibil, N. Godin, M. R’Mili, E. Maillet, G. Fantozzi Optimization of Acoustic Emission Data Clustering by a Genetic Algorithm Method, Springer-Verlag, Journal of Nondestructive Evaluation, Volume 31, Issue 2, pp 169–180(2012).
Lleti, R., Ortiz, M.C., Sarabia, L.A., et al.: Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes. Anal. Chim. Acta 515, pp. 87–100 (2004).
AnutoshPratap Singh, Jitendra Agrawal, Varsha Sharma An Efficient Approach to Enhance Classifier and Cluster Ensembles Using Genetic algorithms for Mining Drifting Data Streams, IJCA (0975–8887), Vol. 44, No. 21(2012).
M. Imran, H. Jabeen, M. Ahmad, Q. Abbas, and W. Bangyal, “Opposition based PSO and mutation operators,” in Proceedings of the 2nd International Conference on Education Technology and Computer (ICETC ‘10), pp. V4506–V4508 (2010).
Tansel Özyer, Reda Alhajj, Parallel clustering of high dimensional data by integrating multiobjective genetic algorithm with divide and conquer, Springer US, Vol. 31, pp. 318–331, 2009.
M.Imran, R. Hashim, and N. E. A. Khalid, “An overview of particle swarm Optimization variants,” Procedia Engineering, vol. 53, pp. 491–496 (2013).
Painho, M., Fernando, B.: Using genetic algorithms in clustering problems. In: Proceedings of the 5th International Conference on GeoComputation (2000).
Garai, G., Chaudhury, B.B.: A novel genetic algorithm for automatic clustering. Pattern Recognition Letters 25, 173–187 (2004).
Jones D, Beltramo, Solving partitioning problems with genetic algorithms. In Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 442–449 (1991).
Nirmalya Chowdhury, Premananda Jana Finding the Natural Groupings in a Data Set Using Genetic Algorithms, Springer Berlin Heidelberg, Applied Computing, Lecture Notes in Computer Science Volume 3285, pp. 26–33(2004).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Gandamalla, M.D., Maddala, S., Sunitha, K.V.N. (2016). A Novel Genetic Algorithm and Particle Swarm Optimization for Data Clustering. In: Satapathy, S.C., Mandal, J.K., Udgata, S.K., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 434. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2752-6_19
Download citation
DOI: https://doi.org/10.1007/978-81-322-2752-6_19
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2750-2
Online ISBN: 978-81-322-2752-6
eBook Packages: EngineeringEngineering (R0)