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Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics

by Y. S. Thakare, S. B. Bagal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 110 - Number 11
Year of Publication: 2015
Authors: Y. S. Thakare, S. B. Bagal
10.5120/19360-0929

Y. S. Thakare, S. B. Bagal . Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics. International Journal of Computer Applications. 110, 11 ( January 2015), 12-16. DOI=10.5120/19360-0929

@article{ 10.5120/19360-0929,
author = { Y. S. Thakare, S. B. Bagal },
title = { Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 110 },
number = { 11 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 12-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume110/number11/19360-0929/ },
doi = { 10.5120/19360-0929 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:46:04.928719+05:30
%A Y. S. Thakare
%A S. B. Bagal
%T Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics
%J International Journal of Computer Applications
%@ 0975-8887
%V 110
%N 11
%P 12-16
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cluster analysis has been widely used in several disciplines, such as statistics, software engineering, biology, psychology and other social sciences, in order to identify natural groups in large amounts of data. Clustering has also been widely adopted by researchers within computer science and especially the database community. K-means is the most famous clustering algorithms. In this paper, the performance of basic k means algorithm is evaluated using various distance metrics for iris dataset, wine dataset, vowel dataset, ionosphere dataset and crude oil dataset by varying no of clusters. From the result analysis we can conclude that the performance of k means algorithm is based on the distance metrics for selected database. Thus, this work will help to select suitable distance metric for particular application.

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Index Terms

Computer Science
Information Sciences

Keywords

Clustering algorithms Pattern recognition.

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