A Hybrid Heuristic for the k-medoids Clustering Problem
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNIFESP |
Texto Completo: | http://repositorio.unifesp.br/handle/11600/34362 http://dx.doi.org/10.1145/2330163.2330223 |
Resumo: | Clustering is an important tool for data analysis, since it allows the exploration of datasets with no or very little prior information. Its main goal is to group a set of data based on their similarity (dissimilarity). A well known mathematical formulation for clustering is the k-medoids problem. Current versions of k-medoids rely on heuristics, with good results reported in the literature. However, few methods that analyze the quality of the partitions found by the heuristics have been proposed. in this paper, we propose a hybrid Lagrangian heuristic for the k-medoids. We compare the performance of the proposed Lagrangian heuristic with other heuristics for the k-medoids problem found in literature. Experimental results presented that the proposed Lagrangian heuristic outperformed the other algorithms. |
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Nascimento, Maria C. V. [UNIFESP]Toledo, Franklina M. B.Carvalho, Andre C. P. L. F. deSoule, T.Universidade Federal de São Paulo (UNIFESP)2016-01-24T14:17:36Z2016-01-24T14:17:36Z2012-01-01Proceedings of the Fourteenth International Conference On Genetic and Evolutionary Computation Conference. New York: Assoc Computing Machinery, p. 417-424, 2012.http://repositorio.unifesp.br/handle/11600/34362http://dx.doi.org/10.1145/2330163.2330223WOS000309611100053.pdf10.1145/2330163.2330223WOS:000309611100053Clustering is an important tool for data analysis, since it allows the exploration of datasets with no or very little prior information. Its main goal is to group a set of data based on their similarity (dissimilarity). A well known mathematical formulation for clustering is the k-medoids problem. Current versions of k-medoids rely on heuristics, with good results reported in the literature. However, few methods that analyze the quality of the partitions found by the heuristics have been proposed. in this paper, we propose a hybrid Lagrangian heuristic for the k-medoids. We compare the performance of the proposed Lagrangian heuristic with other heuristics for the k-medoids problem found in literature. Experimental results presented that the proposed Lagrangian heuristic outperformed the other algorithms.UNIFESP, Inst Ciencia & Tecnol, BR-12230280 Sao Jose Dos Campos, SP, BrazilUNIFESP, Inst Ciencia & Tecnol, ICT, BR-12230280 Sao Jose Dos Campos, SP, BrazilWeb of Science417-424engAssoc Computing MachineryProceedings of the Fourteenth International Conference On Genetic and Evolutionary Computation ConferenceclusteringbioinformaticsheuristicPAMinteger programmingA Hybrid Heuristic for the k-medoids Clustering Probleminfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UNIFESPinstname:Universidade Federal de São Paulo (UNIFESP)instacron:UNIFESPORIGINALWOS000309611100053.pdfapplication/pdf432987${dspace.ui.url}/bitstream/11600/34362/1/WOS000309611100053.pdf4324a8c8a814d0396f5fd590aa9ed93aMD51open accessTEXTWOS000309611100053.pdf.txtWOS000309611100053.pdf.txtExtracted texttext/plain37060${dspace.ui.url}/bitstream/11600/34362/9/WOS000309611100053.pdf.txt86b95d541f3f75f5d97c4bb8c3dd4249MD59open accessTHUMBNAILWOS000309611100053.pdf.jpgWOS000309611100053.pdf.jpgIM Thumbnailimage/jpeg6099${dspace.ui.url}/bitstream/11600/34362/11/WOS000309611100053.pdf.jpg95999f1df3b29a5f629f216ebd0afd4cMD511open access11600/343622023-06-05 19:11:59.473open accessoai:repositorio.unifesp.br:11600/34362Repositório InstitucionalPUBhttp://www.repositorio.unifesp.br/oai/requestopendoar:34652023-06-05T22:11:59Repositório Institucional da UNIFESP - Universidade Federal de São Paulo (UNIFESP)false |
dc.title.en.fl_str_mv |
A Hybrid Heuristic for the k-medoids Clustering Problem |
title |
A Hybrid Heuristic for the k-medoids Clustering Problem |
spellingShingle |
A Hybrid Heuristic for the k-medoids Clustering Problem Nascimento, Maria C. V. [UNIFESP] clustering bioinformatics heuristic PAM integer programming |
title_short |
A Hybrid Heuristic for the k-medoids Clustering Problem |
title_full |
A Hybrid Heuristic for the k-medoids Clustering Problem |
title_fullStr |
A Hybrid Heuristic for the k-medoids Clustering Problem |
title_full_unstemmed |
A Hybrid Heuristic for the k-medoids Clustering Problem |
title_sort |
A Hybrid Heuristic for the k-medoids Clustering Problem |
author |
Nascimento, Maria C. V. [UNIFESP] |
author_facet |
Nascimento, Maria C. V. [UNIFESP] Toledo, Franklina M. B. Carvalho, Andre C. P. L. F. de Soule, T. |
author_role |
author |
author2 |
Toledo, Franklina M. B. Carvalho, Andre C. P. L. F. de Soule, T. |
author2_role |
author author author |
dc.contributor.institution.none.fl_str_mv |
Universidade Federal de São Paulo (UNIFESP) |
dc.contributor.author.fl_str_mv |
Nascimento, Maria C. V. [UNIFESP] Toledo, Franklina M. B. Carvalho, Andre C. P. L. F. de Soule, T. |
dc.subject.eng.fl_str_mv |
clustering bioinformatics heuristic PAM integer programming |
topic |
clustering bioinformatics heuristic PAM integer programming |
description |
Clustering is an important tool for data analysis, since it allows the exploration of datasets with no or very little prior information. Its main goal is to group a set of data based on their similarity (dissimilarity). A well known mathematical formulation for clustering is the k-medoids problem. Current versions of k-medoids rely on heuristics, with good results reported in the literature. However, few methods that analyze the quality of the partitions found by the heuristics have been proposed. in this paper, we propose a hybrid Lagrangian heuristic for the k-medoids. We compare the performance of the proposed Lagrangian heuristic with other heuristics for the k-medoids problem found in literature. Experimental results presented that the proposed Lagrangian heuristic outperformed the other algorithms. |
publishDate |
2012 |
dc.date.issued.fl_str_mv |
2012-01-01 |
dc.date.accessioned.fl_str_mv |
2016-01-24T14:17:36Z |
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2016-01-24T14:17:36Z |
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info:eu-repo/semantics/publishedVersion |
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dc.identifier.citation.fl_str_mv |
Proceedings of the Fourteenth International Conference On Genetic and Evolutionary Computation Conference. New York: Assoc Computing Machinery, p. 417-424, 2012. |
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http://repositorio.unifesp.br/handle/11600/34362 http://dx.doi.org/10.1145/2330163.2330223 |
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WOS000309611100053.pdf |
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10.1145/2330163.2330223 |
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WOS:000309611100053 |
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Proceedings of the Fourteenth International Conference On Genetic and Evolutionary Computation Conference. New York: Assoc Computing Machinery, p. 417-424, 2012. WOS000309611100053.pdf 10.1145/2330163.2330223 WOS:000309611100053 |
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http://repositorio.unifesp.br/handle/11600/34362 http://dx.doi.org/10.1145/2330163.2330223 |
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eng |
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eng |
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Proceedings of the Fourteenth International Conference On Genetic and Evolutionary Computation Conference |
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Assoc Computing Machinery |
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Assoc Computing Machinery |
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