CFP last date
22 July 2024
Call for Paper
August Edition
IJCA solicits high quality original research papers for the upcoming August edition of the journal. The last date of research paper submission is 22 July 2024

Submit your paper
Know more
The week's pick
Responsive image
Implementation of Polynomial Regression using Least Squares and Gradient Descent in Python

Ahmad Farhan AlShammari
Random Articles
Reseach Article

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

by Arpita Agrawal, Hitesh Gupta
journal cover thumbnail

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

Powered by PhDFocusTM