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Comparison between Standard K-Mean Clustering and Improved K-Mean Clustering

by Pooja Pandey, Ishpreet Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 13
Year of Publication: 2016
Authors: Pooja Pandey, Ishpreet Singh
10.5120/ijca2016910868

Pooja Pandey, Ishpreet Singh . Comparison between Standard K-Mean Clustering and Improved K-Mean Clustering. International Journal of Computer Applications. 146, 13 ( Jul 2016), 39-42. DOI=10.5120/ijca2016910868

@article{ 10.5120/ijca2016910868,
author = { Pooja Pandey, Ishpreet Singh },
title = { Comparison between Standard K-Mean Clustering and Improved K-Mean Clustering },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 13 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 39-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number13/25462-2016910868/ },
doi = { 10.5120/ijca2016910868 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:50:24.897238+05:30
%A Pooja Pandey
%A Ishpreet Singh
%T Comparison between Standard K-Mean Clustering and Improved K-Mean Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 13
%P 39-42
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering in data mining is very important to discover distribution patterns and this importance tends to increase as the amount of data grows. It is one of the main analytical methods in data mining and its method influences its results directly. K-means is a typical clustering algorithm[3]. It mainly consists of two phases i.e. initializing random clusters and to find the nearest neighbour. Both phases have some shortcomings which are discussed in the paper and two methods are purposed based on that. First one is about how to generate the centroids and the second one will reduce the time while calculating distance from centroid.

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Index Terms

Computer Science
Information Sciences

Keywords

K-Mean Clustering

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