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

by Arpita Agrawal, Hitesh Gupta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 79 - Number 2
Year of Publication: 2013
Authors: Arpita Agrawal, Hitesh Gupta
10.5120/13713-1472

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 },
issue_date = { October 2013 },
volume = { 79 },
number = { 2 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume79/number2/13713-1472/ },
doi = { 10.5120/13713-1472 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:51:58.547051+05:30
%A Arpita Agrawal
%A Hitesh Gupta
%T Global K-Means (GKM) Clustering Algorithm: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 79
%N 2
%P 20-24
%D 2013
%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
  1. 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
  2. A. Likas, M. Vlassis, and J. Verbeek, "The global k-means clustering algorithm," Pattern Recognition, vol. 36, pp. 451–461, 2003.
  3. G. Tzortzis and A. Likas "The Global Kernel k-Means Clustering Algorithm" International Joint Conference on Neural Networks (IJCNN 2008), 2008, pp 1978-1985
  4. 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
  5. M. N. Murty, A. K. Jain, P. J. Flynn, Data clustering: a review, ACM Comput. Surv. 31 (3) (1999) 264–323
  6. 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
  7. Wang, J. and X. Su, 2011. "An improved K-means clustering algorithm," in 3rd International Conference on Communication Software and Networks (ICCSN), Xi'an.
  8. 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.
  9. 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.
  10. 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.
  11. Bagirov, Adil M. "Modified global k-means algorithm for minimum sum-of-squares clustering problems. " Pattern Recognition 41, no. 10 (2008): 3192-3199.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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

Keywords

Clustering K-means GKM FGKM Streamed Dataset

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