An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters

Autores
Carballido, Jessica Andrea; Latini, Macarena Anahí; Ponzoni, Ignacio; Cecchini, Rocío Luján
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
One of the main problems being faced at the time of performing data clustering consists in the deteremination of the best clustering method together with defining the ideal amount (k) of groups in which these data should be separated. In this paper, a preliminary approximation of a clustering recommender method is presented which, starting from a set of standardized data, suggests the best clustering strategy and also proposes an advisable k value. For this aim, the algorithm considers four indices for evaluating the final structure of clusters: Dunn, Silhouette, Widest Gap and Entropy. The prototype is implemented as a Genetic Algorithm in which individuals are possible configurations of the methods and their parameters. In this first prototype, the algorithm suggests between four partitioning methods namely K-means, PAM, CLARA and, Fanny. Also, the best set of parameters to execute the suggested method is obtained. The prototype was developed in an R environment, and its findings could be corroborated as consistent when compared with a combination of results provided by other methods with similar objectives. The idea of this prototype is to serve as the initial basis for a more complex framework that also incorporates the reduction of matrices with vast numbers of rows.
Fil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Latini, Macarena Anahí. Universidad Nacional del Sur; Argentina
Fil: Ponzoni, Ignacio. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cecchini, Rocío Luján. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Materia
CLUSTERING RECOMMENDATION METHODS
EVOLUTIONARY ALGORITHMS
PARTITION CLUSTERING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/91533

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spelling An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its ParametersCarballido, Jessica AndreaLatini, Macarena AnahíPonzoni, IgnacioCecchini, Rocío LujánCLUSTERING RECOMMENDATION METHODSEVOLUTIONARY ALGORITHMSPARTITION CLUSTERINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1One of the main problems being faced at the time of performing data clustering consists in the deteremination of the best clustering method together with defining the ideal amount (k) of groups in which these data should be separated. In this paper, a preliminary approximation of a clustering recommender method is presented which, starting from a set of standardized data, suggests the best clustering strategy and also proposes an advisable k value. For this aim, the algorithm considers four indices for evaluating the final structure of clusters: Dunn, Silhouette, Widest Gap and Entropy. The prototype is implemented as a Genetic Algorithm in which individuals are possible configurations of the methods and their parameters. In this first prototype, the algorithm suggests between four partitioning methods namely K-means, PAM, CLARA and, Fanny. Also, the best set of parameters to execute the suggested method is obtained. The prototype was developed in an R environment, and its findings could be corroborated as consistent when compared with a combination of results provided by other methods with similar objectives. The idea of this prototype is to serve as the initial basis for a more complex framework that also incorporates the reduction of matrices with vast numbers of rows.Fil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Latini, Macarena Anahí. Universidad Nacional del Sur; ArgentinaFil: Ponzoni, Ignacio. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cecchini, Rocío Luján. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaElsevier2018-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/91533Carballido, Jessica Andrea; Latini, Macarena Anahí; Ponzoni, Ignacio; Cecchini, Rocío Luján; An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters; Elsevier; Electronic Notes in Discrete Mathematics; 69; 8-2018; 229-2361571-0653CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1571065318301744info:eu-repo/semantics/altIdentifier/doi/10.1016/j.endm.2018.07.030info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:05:46Zoai:ri.conicet.gov.ar:11336/91533instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:05:46.325CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters
title An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters
spellingShingle An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters
Carballido, Jessica Andrea
CLUSTERING RECOMMENDATION METHODS
EVOLUTIONARY ALGORITHMS
PARTITION CLUSTERING
title_short An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters
title_full An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters
title_fullStr An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters
title_full_unstemmed An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters
title_sort An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters
dc.creator.none.fl_str_mv Carballido, Jessica Andrea
Latini, Macarena Anahí
Ponzoni, Ignacio
Cecchini, Rocío Luján
author Carballido, Jessica Andrea
author_facet Carballido, Jessica Andrea
Latini, Macarena Anahí
Ponzoni, Ignacio
Cecchini, Rocío Luján
author_role author
author2 Latini, Macarena Anahí
Ponzoni, Ignacio
Cecchini, Rocío Luján
author2_role author
author
author
dc.subject.none.fl_str_mv CLUSTERING RECOMMENDATION METHODS
EVOLUTIONARY ALGORITHMS
PARTITION CLUSTERING
topic CLUSTERING RECOMMENDATION METHODS
EVOLUTIONARY ALGORITHMS
PARTITION CLUSTERING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv One of the main problems being faced at the time of performing data clustering consists in the deteremination of the best clustering method together with defining the ideal amount (k) of groups in which these data should be separated. In this paper, a preliminary approximation of a clustering recommender method is presented which, starting from a set of standardized data, suggests the best clustering strategy and also proposes an advisable k value. For this aim, the algorithm considers four indices for evaluating the final structure of clusters: Dunn, Silhouette, Widest Gap and Entropy. The prototype is implemented as a Genetic Algorithm in which individuals are possible configurations of the methods and their parameters. In this first prototype, the algorithm suggests between four partitioning methods namely K-means, PAM, CLARA and, Fanny. Also, the best set of parameters to execute the suggested method is obtained. The prototype was developed in an R environment, and its findings could be corroborated as consistent when compared with a combination of results provided by other methods with similar objectives. The idea of this prototype is to serve as the initial basis for a more complex framework that also incorporates the reduction of matrices with vast numbers of rows.
Fil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Latini, Macarena Anahí. Universidad Nacional del Sur; Argentina
Fil: Ponzoni, Ignacio. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cecchini, Rocío Luján. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
description One of the main problems being faced at the time of performing data clustering consists in the deteremination of the best clustering method together with defining the ideal amount (k) of groups in which these data should be separated. In this paper, a preliminary approximation of a clustering recommender method is presented which, starting from a set of standardized data, suggests the best clustering strategy and also proposes an advisable k value. For this aim, the algorithm considers four indices for evaluating the final structure of clusters: Dunn, Silhouette, Widest Gap and Entropy. The prototype is implemented as a Genetic Algorithm in which individuals are possible configurations of the methods and their parameters. In this first prototype, the algorithm suggests between four partitioning methods namely K-means, PAM, CLARA and, Fanny. Also, the best set of parameters to execute the suggested method is obtained. The prototype was developed in an R environment, and its findings could be corroborated as consistent when compared with a combination of results provided by other methods with similar objectives. The idea of this prototype is to serve as the initial basis for a more complex framework that also incorporates the reduction of matrices with vast numbers of rows.
publishDate 2018
dc.date.none.fl_str_mv 2018-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/91533
Carballido, Jessica Andrea; Latini, Macarena Anahí; Ponzoni, Ignacio; Cecchini, Rocío Luján; An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters; Elsevier; Electronic Notes in Discrete Mathematics; 69; 8-2018; 229-236
1571-0653
CONICET Digital
CONICET
url http://hdl.handle.net/11336/91533
identifier_str_mv Carballido, Jessica Andrea; Latini, Macarena Anahí; Ponzoni, Ignacio; Cecchini, Rocío Luján; An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters; Elsevier; Electronic Notes in Discrete Mathematics; 69; 8-2018; 229-236
1571-0653
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1571065318301744
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.endm.2018.07.030
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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