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Optimizing k-means for Scalability

by Akansha Agrawal, Shreya Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 17
Year of Publication: 2015
Authors: Akansha Agrawal, Shreya Sharma
10.5120/21320-4337

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 },
issue_date = { June 2015 },
volume = { 120 },
number = { 17 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number17/21320-4337/ },
doi = { 10.5120/21320-4337 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:06:29.089383+05:30
%A Akansha Agrawal
%A Shreya Sharma
%T Optimizing k-means for Scalability
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 17
%P 20-24
%D 2015
%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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Index Terms

Computer Science
Information Sciences

Keywords

Data mining Big Data k-means

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