![]() |
10.5120/21184-4258 |
Malini G Devi, M.seetha and K.v.n.sunitha. Article: A Novel Hybrid Clustering Techniques based on K-Means, PSO and Dynamic Optimization. International Journal of Computer Applications 119(20):20-25, June 2015. Full text available. BibTeX
@article{key:article, author = {G. Malini Devi and M.seetha and K.v.n.sunitha}, title = {Article: A Novel Hybrid Clustering Techniques based on K-Means, PSO and Dynamic Optimization}, journal = {International Journal of Computer Applications}, year = {2015}, volume = {119}, number = {20}, pages = {20-25}, month = {June}, note = {Full text available} }
Abstract
Clustering is a process for partitioning datasets. This technique is a challenging field of research in which their potential applications pose their own special requirements. K-Means is the most extensively used algorithm to find a partition that minimizes Mean Square Error (MSE) is an exigent task. The Object Function of the K-Means is not convex and hence it may contain local minima. ACO methods are useful in problems that need to find paths to goals. Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. But PSO algorithm suffers from slow convergence near optimal solution. In this paper a new modified sequential clustering approach is proposed, which uses PSO in combination with K-Means & dynamic optimization algorithm for data clustering. This approach overcomes drawbacks of K-means, PSO technique, improves clustering and avoids being trapped in a local optimal solution. It was ascertained that the K-Means, PSO, KPSOK & dynamic optimization algorithms are proposed among these algorithms dynamic optimization results in accurate, robust and better clustering.
References
- Xiaohui Huang; Shenzhen Grad. Sch. , Yunming Ye ; Haijun Zhang Extensions of Kmeans-Type Algorithms: A New Clustering Framework by Integrating Intracluster Compactness and Intercluster Separation IEEE Transcations on Neural Networks and Learning systems, Volume:25 , Issue: 8, Aug. 2014, pg no1433 – 1446.
- ZhouHong-bo , Daqing, China Gao Jun-tao An automatic clustering method based on distance evaluation function- 2014 IEEE Workshop on Electronics, Computer and Applications- 2014, page no-10. 1109/IWECA. 2014. 6845701.
- Jayshree Ghorpade-Aher, Vishakha Arun Metre, PSO based Multidimensional Data Clustering: A Survey, International Journal of Computer Applications (0975 -8887),Volume 87 – No. 16, February 2014.
- M. Imran, R. Hashim, and N. E. A. Khalid, "An overview of particle swarm optimization variants,"Procedia Engineering, vol. 53, pp. 491–496, 2013.
- S. C. Satapathy, G. Pradhan, S. Pattnaik, J. V. R. Murthy, and P. V. G. D. P. Reddy, "Performance comparisons of PSO based clustering," InterJRI Computer Science and Networking, vol. 1, no. 1, pp. 18–23, 2009.
- Joshua Zhexue Huang,Michael K. Ng, Hongqiang Rong, Zichen Li . Automated Variable Weighting in k-Means Type Clustering[J], IEEE Transactions on Pattern Analysis and Maching Intelligence, 2005, 27(5):657-668.
- Chen, Ching-Yi. and Ye, Fun. , "Particle Swarm Optimization Algorithm and Its Application to Clustering Analysis," IEEE ICNSC 2004, Taipei, Taiwan, R. O. C. ,pp. 789_794 (2004).
- Van den Bargh, F. ; Engelbrecht, A. P. A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comp. 2004, 8, 225–239.
- Coello, C. A. C. ; Pulido, G. T. ; Lechuga, M. S. Handling Multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 2004, 8, 240–255.
- Chen, Ching-Yi. and Ye, Fun. , "K-means Algorithm Based on Particle Swarm Optimization," 2003 International Conference on Informatics, Cybernetics, and Systems, I-Shou University, Taiwan, R. O. C. pp. 1470?1475 (2003).
- Eberhart, R. C. and Shi, Y. , "Particle Swarm Optimization:Developments, Applications and Resources," Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2001), Seoul, Korea (2001).
- R. Eberhart, J. Kennedy, "A new optimizer using particle swarm theory," Proc. 6th Int. Symposium on Micro Machine and Human Science, pp. 39-43, 1995.
- S. Z. Selim, M. A. Ismail, "K-means type algorithms: a generalized convergence theorem and characterization of local optimality," IEEE Trans. Pattern Anal. Mach. Intell. 6, pp. 81-87, 1984.
- Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. 1995. pp. 1942–1948.