Performance Evaluation of Distance Metrics in the Clustering Algorithms
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/21 |
Resumo: | Distance measures play an important role in cluster analysis. There is no single distance measure that best fits for all types of the clustering problems. So, it is important to find set of distance measures for different clustering techniques on datasets that yields optimal results. In this paper, an attempt has been made to evaluate ten different distance measures on eight clustering techniques. The quality of the distance measures has been computed on basis of three factors: accuracy, inter-cluster and intra-cluster distances. The performance of clustering algorithms on different distance measures has been evaluated on three artificial and six real life datasets. The experimental results reveal that the performance and quality of different distance measures vary with the nature of data as well as clustering techniques. Hence choice of distance measure must be done on basis of dataset and clustering technique. |
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INFOCOMP: Jornal de Ciência da Computação |
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Performance Evaluation of Distance Metrics in the Clustering AlgorithmsDistance measures play an important role in cluster analysis. There is no single distance measure that best fits for all types of the clustering problems. Soit is important to find set of distance measures for different clustering techniques on datasetsDistance measures play an important role in cluster analysis. There is no single distance measure that best fits for all types of the clustering problems. So, it is important to find set of distance measures for different clustering techniques on datasets that yields optimal results. In this paper, an attempt has been made to evaluate ten different distance measures on eight clustering techniques. The quality of the distance measures has been computed on basis of three factors: accuracy, inter-cluster and intra-cluster distances. The performance of clustering algorithms on different distance measures has been evaluated on three artificial and six real life datasets. The experimental results reveal that the performance and quality of different distance measures vary with the nature of data as well as clustering techniques. Hence choice of distance measure must be done on basis of dataset and clustering technique.Editora da UFLA2014-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/21INFOCOMP Journal of Computer Science; Vol. 13 No. 1 (2014): June 2014; 38-521982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/21/8Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessKumar, VijayChhabra, Jitender KumarKumar, Dinesh2015-07-29T16:47:21Zoai:infocomp.dcc.ufla.br:article/21Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:11.857509INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
Performance Evaluation of Distance Metrics in the Clustering Algorithms |
title |
Performance Evaluation of Distance Metrics in the Clustering Algorithms |
spellingShingle |
Performance Evaluation of Distance Metrics in the Clustering Algorithms Kumar, Vijay Distance measures play an important role in cluster analysis. There is no single distance measure that best fits for all types of the clustering problems. So it is important to find set of distance measures for different clustering techniques on datasets |
title_short |
Performance Evaluation of Distance Metrics in the Clustering Algorithms |
title_full |
Performance Evaluation of Distance Metrics in the Clustering Algorithms |
title_fullStr |
Performance Evaluation of Distance Metrics in the Clustering Algorithms |
title_full_unstemmed |
Performance Evaluation of Distance Metrics in the Clustering Algorithms |
title_sort |
Performance Evaluation of Distance Metrics in the Clustering Algorithms |
author |
Kumar, Vijay |
author_facet |
Kumar, Vijay Chhabra, Jitender Kumar Kumar, Dinesh |
author_role |
author |
author2 |
Chhabra, Jitender Kumar Kumar, Dinesh |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Kumar, Vijay Chhabra, Jitender Kumar Kumar, Dinesh |
dc.subject.por.fl_str_mv |
Distance measures play an important role in cluster analysis. There is no single distance measure that best fits for all types of the clustering problems. So it is important to find set of distance measures for different clustering techniques on datasets |
topic |
Distance measures play an important role in cluster analysis. There is no single distance measure that best fits for all types of the clustering problems. So it is important to find set of distance measures for different clustering techniques on datasets |
description |
Distance measures play an important role in cluster analysis. There is no single distance measure that best fits for all types of the clustering problems. So, it is important to find set of distance measures for different clustering techniques on datasets that yields optimal results. In this paper, an attempt has been made to evaluate ten different distance measures on eight clustering techniques. The quality of the distance measures has been computed on basis of three factors: accuracy, inter-cluster and intra-cluster distances. The performance of clustering algorithms on different distance measures has been evaluated on three artificial and six real life datasets. The experimental results reveal that the performance and quality of different distance measures vary with the nature of data as well as clustering techniques. Hence choice of distance measure must be done on basis of dataset and clustering technique. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/21 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/21 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/21/8 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 13 No. 1 (2014): June 2014; 38-52 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874739968147456 |