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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/91533
Ver los metadatos del registro completo
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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) |
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CONICET Digital (CONICET) |
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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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1842269927533182976 |
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13.13397 |