Parallel implementation of fuzzy minimals clustering algorithm

Clustering aims to classify different patterns into groups called clusters. Many algorithms for both hard and fuzzy clustering have been developed to deal with exploratory data analysis in many contexts such as image processing, pattern recognition, etc. However, we are witnessing the era of big dat...

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Detalhes bibliográficos
Autores: Timón, Isabel, Soto, Jesús, Cecilia Canales, José María, Pérez Sánchez, Horacio
Formato: artículo
Fecha de publicación:2016
País:España
Recursos:Universidad Católica San Antonio de Murcia (UCAM)
Repositorio:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
OAI Identifier:oai:repositorio.ucam.edu:10952/3043
Acesso em linha:http://hdl.handle.net/10952/3043
Access Level:acceso abierto
Palavra-chave:Parallel fuzzy clustering
Fuzzy clustering
Fuzzy minimals
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spelling Parallel implementation of fuzzy minimals clustering algorithmTimón, IsabelSoto, JesúsCecilia Canales, José MaríaPérez Sánchez, HoracioParallel fuzzy clusteringFuzzy clusteringFuzzy minimalsClustering aims to classify different patterns into groups called clusters. Many algorithms for both hard and fuzzy clustering have been developed to deal with exploratory data analysis in many contexts such as image processing, pattern recognition, etc. However, we are witnessing the era of big data computing where computing resources are becoming the main bottleneck to deal with those large datasets. In this context, sequential algorithms need to be redesigned and even rethought to fully leverage the emergent massively parallel architectures. In this paper, we propose a parallel implementation of the fuzzy minimals clustering algorithm called Parallel Fuzzy Minimal (PFM). Our experimental results reveal linear speed-up of PFM when compared to the sequential counterpart version, keeping very good classification quality.Ingeniería, Industria y Construcción2016info:eu-repo/semantics/articlehttp://hdl.handle.net/10952/3043reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murciainstname:Universidad Católica San Antonio de Murcia (UCAM)Inglésinfo:eu-repo/semantics/openAccessoai:repositorio.ucam.edu:10952/30432026-06-07T18:35:21Z
dc.title.none.fl_str_mv Parallel implementation of fuzzy minimals clustering algorithm
title Parallel implementation of fuzzy minimals clustering algorithm
spellingShingle Parallel implementation of fuzzy minimals clustering algorithm
Timón, Isabel
Parallel fuzzy clustering
Fuzzy clustering
Fuzzy minimals
title_short Parallel implementation of fuzzy minimals clustering algorithm
title_full Parallel implementation of fuzzy minimals clustering algorithm
title_fullStr Parallel implementation of fuzzy minimals clustering algorithm
title_full_unstemmed Parallel implementation of fuzzy minimals clustering algorithm
title_sort Parallel implementation of fuzzy minimals clustering algorithm
dc.creator.none.fl_str_mv Timón, Isabel
Soto, Jesús
Cecilia Canales, José María
Pérez Sánchez, Horacio
author Timón, Isabel
author_facet Timón, Isabel
Soto, Jesús
Cecilia Canales, José María
Pérez Sánchez, Horacio
author_role author
author2 Soto, Jesús
Cecilia Canales, José María
Pérez Sánchez, Horacio
author2_role author
author
author
dc.subject.none.fl_str_mv Parallel fuzzy clustering
Fuzzy clustering
Fuzzy minimals
topic Parallel fuzzy clustering
Fuzzy clustering
Fuzzy minimals
description Clustering aims to classify different patterns into groups called clusters. Many algorithms for both hard and fuzzy clustering have been developed to deal with exploratory data analysis in many contexts such as image processing, pattern recognition, etc. However, we are witnessing the era of big data computing where computing resources are becoming the main bottleneck to deal with those large datasets. In this context, sequential algorithms need to be redesigned and even rethought to fully leverage the emergent massively parallel architectures. In this paper, we propose a parallel implementation of the fuzzy minimals clustering algorithm called Parallel Fuzzy Minimal (PFM). Our experimental results reveal linear speed-up of PFM when compared to the sequential counterpart version, keeping very good classification quality.
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10952/3043
url http://hdl.handle.net/10952/3043
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
instname:Universidad Católica San Antonio de Murcia (UCAM)
instname_str Universidad Católica San Antonio de Murcia (UCAM)
reponame_str RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
collection RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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