An Improved K-Means Clustering Algorithm

Authors(2) :-Ekta Joshi, Dr. D. A. Parikh

This Vast spread of computing technologies has led to abundance of large data sets. Thus, there is a need to find similarities and define groupings among the elements of these big data sets. One of the ways to find these similarities is data clustering. Currently, there exist several data clustering algorithms which differ by their application area and efficiency. Increase in computational power and algorithmic improvements have reduced the time for clustering of big data sets. But it usually happens that big data sets canít be processed whole due to hardware and computational restrictions. Clustering techniques, like K-Means are useful in analyzing data in a parallel fashion. K-Means largely depends upon a proper initialization to produce optimal results.

Authors and Affiliations

Ekta Joshi
Computer Engineering, L.D. College of Engineering, Ahmedabad, India
Dr. D. A. Parikh
HOD Computer Engineering, L.D. College of Engineering, Ahmedabad, India

K means, Clustering, Data Mining, Big Data.

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Publication Details

Published in : Volume 4 | Issue 2 | January-February 2018
Date of Publication : 2018-01-20
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 239-244
Manuscript Number : IJSRSET184240
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Ekta Joshi, Dr. D. A. Parikh, " An Improved K-Means Clustering Algorithm, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 2, pp.239-244, January-February.2018

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