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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.

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Index Terms

Computer Science
Information Sciences

Keywords

Clustering K-means GKM FGKM Streamed Dataset

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