Research Article

Global K-Means (GKM) Clustering Algorithm: A Survey

by  Arpita Agrawal, Hitesh Gupta
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 79 - Issue 2
Published: October 2013
Authors: Arpita Agrawal, Hitesh Gupta
10.5120/13713-1472
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Arpita Agrawal, Hitesh Gupta . Global K-Means (GKM) Clustering Algorithm: A Survey. International Journal of Computer Applications. 79, 2 (October 2013), 20-24. DOI=10.5120/13713-1472

                        @article{ 10.5120/13713-1472,
                        author  = { Arpita Agrawal,Hitesh Gupta },
                        title   = { Global K-Means (GKM) Clustering Algorithm: A Survey },
                        journal = { International Journal of Computer Applications },
                        year    = { 2013 },
                        volume  = { 79 },
                        number  = { 2 },
                        pages   = { 20-24 },
                        doi     = { 10.5120/13713-1472 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2013
                        %A Arpita Agrawal
                        %A Hitesh Gupta
                        %T Global K-Means (GKM) Clustering Algorithm: A Survey%T 
                        %J International Journal of Computer Applications
                        %V 79
                        %N 2
                        %P 20-24
                        %R 10.5120/13713-1472
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

K-means clustering is a popular clustering algorithm but is having some problems as initial conditions and it will fuse in local minima. A method was proposed to overcome this problem known as Global K-Means clustering algorithm (GKM). This algorithm has excellent skill to reduce the computational load without significantly affecting the solution quality. We studied GKM and its variants and presents a survey with critical analysis. We also proposed a new concept of Faster Global K-means algorithms for Streamed Data sets (FGKM-SD). FGKM-SD improves the efficiency of clustering and will take low time & storage space.

References
  • JuanyingXie and Shuai Jiang. A simple and fast algorithm for global K-means clustering, Second International Workshop on Education Technology and Computer Science, pp 36-40 2010
  • A. Likas, M. Vlassis, and J. Verbeek, "The global k-means clustering algorithm," Pattern Recognition, vol. 36, pp. 451–461, 2003.
  • G. Tzortzis and A. Likas "The Global Kernel k-Means Clustering Algorithm" International Joint Conference on Neural Networks (IJCNN 2008), 2008, pp 1978-1985
  • J. A. Lozano, J. M. Pena, P. Larranaga, An empirical comparison of four initialization methods for the k-means algorithm, Pattern Recognition Lett. 20 (1999) 1027–1040
  • M. N. Murty, A. K. Jain, P. J. Flynn, Data clustering: a review, ACM Comput. Surv. 31 (3) (1999) 264–323
  • Na, S. and L. Xumin, 2010. "Research on K-means Clustering Algorithm An Improved K-means Clustering Algorithm," in Third International Symposium on Intelligent Information Technology and Security Informatics (IITSI), Jinggangshan
  • Wang, J. and X. Su, 2011. "An improved K-means clustering algorithm," in 3rd International Conference on Communication Software and Networks (ICCSN), Xi'an.
  • P. S. Bradley and U. M. Fayyad, "Refining initial points for k-means clustering," Proceedings of the Fifteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc. San Francisco, CA, USA, 1998, pp. 91–99.
  • Bagirov, Adil M. , and KarimMardaneh. "Modified global k-means algorithm for clustering in gene expression data sets. " In Proceedings of the 2006 workshop on Intelligent systems for bioinformatics-Volume 73, pp. 23-28. Australian Computer Society, Inc. , 2006.
  • Chang, Roy Kwang Yang, Chu Kiong Loo, and M. V. C. Rao. "A Global k-means Approach for Autonomous Cluster Initialization of Probabilistic Neural Network. " Informatica (Slovenia) 32, no. 2 (2008): 219-225.
  • Bagirov, Adil M. "Modified global k-means algorithm for minimum sum-of-squares clustering problems. " Pattern Recognition 41, no. 10 (2008): 3192-3199.
  • Kumar, Parvesh, and SiriKrishanWasan. "Analysis of X-means and global k-means USING TUMOR classification. " In Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on, vol. 5, pp. 832-835. IEEE, 2010.
  • Xie, Juanying, and Shuai Jiang. "A simple and fast algorithm for global K-means clustering. " In Education Technology and Computer Science (ETCS), 2010 Second International Workshop on, vol. 2, pp. 36-40. IEEE, 2010.
  • BAGIROV, Adil M. , Julien UGON, and Dean WEBB. "Fast modified global k-means algorithm for incremental cluster construction. " Pattern recognition 44, no. 4 (2011): 866-876.
  • Lai, Jim ZC, and Tsung-Jen Huang. "Fast global k-means clustering using cluster membership and inequality. " Pattern Recognition 43, no. 5 (2010): 1954-1963.
  • Wang, Lidong, Xiaodong Liu, and Yashuang Mu. "The Global k-Means Clustering Analysis Based on Multi-Granulations Nearness Neighborhood. " Mathematics in computer science 7, no. 1 (2013): 113-124.
  • Bai, Liang, Jiye Liang, Chao Sui, and Chuangyin Dang. "Fast global k-means clustering based on local geometrical information. " Information Sciences (2013).
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Clustering K-means GKM FGKM Streamed Dataset

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