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K-means with Three different Distance Metrics

by Archana Singh, Avantika Yadav, Ajay Rana
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 10
Year of Publication: 2013
Authors: Archana Singh, Avantika Yadav, Ajay Rana
10.5120/11430-6785

Archana Singh, Avantika Yadav, Ajay Rana . K-means with Three different Distance Metrics. International Journal of Computer Applications. 67, 10 ( April 2013), 13-17. DOI=10.5120/11430-6785

@article{ 10.5120/11430-6785,
author = { Archana Singh, Avantika Yadav, Ajay Rana },
title = { K-means with Three different Distance Metrics },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 10 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number10/11430-6785/ },
doi = { 10.5120/11430-6785 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:24:17.802230+05:30
%A Archana Singh
%A Avantika Yadav
%A Ajay Rana
%T K-means with Three different Distance Metrics
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 10
%P 13-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The power of k-means algorithm is due to its computational efficiency and the nature of ease at which it can be used. Distance metrics are used to find similar data objects that lead to develop robust algorithms for the data mining functionalities such as classification and clustering. In this paper, the results obtained by implementing the k-means algorithm using three different metrics Euclidean, Manhattan and Minkowski distance metrics along with the comparative study of results of basic k-means algorithm which is implemented through Euclidian distance metric for two-dimensional data, are discussed. Results are displayed with the help of histograms.

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Index Terms

Computer Science
Information Sciences

Keywords

Centroids clustering distortion metrics similarity matrix

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