Research Article
Optimizing k-means for Scalability
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 120 - Issue 17 |
| Published: June 2015 |
| Authors: Akansha Agrawal, Shreya Sharma |
10.5120/21320-4337
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Akansha Agrawal, Shreya Sharma . Optimizing k-means for Scalability. International Journal of Computer Applications. 120, 17 (June 2015), 20-24. DOI=10.5120/21320-4337
@article{ 10.5120/21320-4337,
author = { Akansha Agrawal,Shreya Sharma },
title = { Optimizing k-means for Scalability },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 120 },
number = { 17 },
pages = { 20-24 },
doi = { 10.5120/21320-4337 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Akansha Agrawal
%A Shreya Sharma
%T Optimizing k-means for Scalability%T
%J International Journal of Computer Applications
%V 120
%N 17
%P 20-24
%R 10.5120/21320-4337
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Proposed decades ago, k-means is still the most popular algorithm for clustering. Despite the drawbacks of k-means, its advantages make it most attractive. Several researches have been conducted to alleviate the problems of k-means. We suggest here some simple modifications to optimize k-means for scalability without much sacrifice in the precision. Current shift in emphasis of data mining towards Big Data requires fast algorithms that can scale well. We propose an idea how time-tested techniques can be adapted to changing needs. The implementation results demonstrate the impact simple modifications can bring
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