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: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/15008 |
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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Performance evaluation of distance metrics in the clustering algorithmsDistance measuresClustering algorithmsAnt colony based clusteringModified harmony search clusteringDistance 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.Universidade Federal de Lavras (UFLA)2014-09-012017-08-01T21:08:45Z2017-08-01T21:08:45Z2017-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfKUMAR, V.; CHHABRA, J. K.; KUMAR, D. Performance evaluation of distance metrics in the clustering algorithms. INFOCOMP Journal of Computer Science, Lavras, v. 13, n. 1, p. 38-52, Sept. 2014.http://repositorio.ufla.br/jspui/handle/1/15008INFOCOMP; Vol 13 No 1 (2014): June 2014; 38-521982-33631807-4545reponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttp://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/21/8Copyright (c) 2016 INFOCOMP Journal of Computer ScienceAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessKumar, VijayChhabra, Jitender KumarKumar, Dinesh2021-09-14T23:47:40Zoai:localhost:1/15008Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2021-09-14T23:47:40Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
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 Clustering algorithms Ant colony based clustering Modified harmony search clustering |
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 Clustering algorithms Ant colony based clustering Modified harmony search clustering |
topic |
Distance measures Clustering algorithms Ant colony based clustering Modified harmony search clustering |
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 2017-08-01T21:08:45Z 2017-08-01T21:08:45Z 2017-08-01 |
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 |
KUMAR, V.; CHHABRA, J. K.; KUMAR, D. Performance evaluation of distance metrics in the clustering algorithms. INFOCOMP Journal of Computer Science, Lavras, v. 13, n. 1, p. 38-52, Sept. 2014. http://repositorio.ufla.br/jspui/handle/1/15008 |
identifier_str_mv |
KUMAR, V.; CHHABRA, J. K.; KUMAR, D. Performance evaluation of distance metrics in the clustering algorithms. INFOCOMP Journal of Computer Science, Lavras, v. 13, n. 1, p. 38-52, Sept. 2014. |
url |
http://repositorio.ufla.br/jspui/handle/1/15008 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/21/8 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Lavras (UFLA) |
publisher.none.fl_str_mv |
Universidade Federal de Lavras (UFLA) |
dc.source.none.fl_str_mv |
INFOCOMP; Vol 13 No 1 (2014): June 2014; 38-52 1982-3363 1807-4545 reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835184827990016 |