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dc.contributor.author
Carballido, Jessica Andrea
dc.contributor.author
Latini, Macarena Anahí
dc.contributor.author
Ponzoni, Ignacio
dc.contributor.author
Cecchini, Rocío Luján
dc.date.available
2019-12-05T19:27:20Z
dc.date.issued
2018-08
dc.identifier.citation
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
dc.identifier.issn
1571-0653
dc.identifier.uri
http://hdl.handle.net/11336/91533
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
CLUSTERING RECOMMENDATION METHODS
dc.subject
EVOLUTIONARY ALGORITHMS
dc.subject
PARTITION CLUSTERING
dc.subject.classification
Ciencias de la Información y Bioinformática
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-10-23T17:31:39Z
dc.journal.volume
69
dc.journal.pagination
229-236
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
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
dc.description.fil
Fil: Latini, Macarena Anahí. Universidad Nacional del Sur; Argentina
dc.description.fil
Fil: Ponzoni, Ignacio. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
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
dc.journal.title
Electronic Notes in Discrete Mathematics
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1571065318301744
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.endm.2018.07.030


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