Adaptive clustering with artificial ants

Autores
Ingaramo, Diego Alejandro; Leguizamón, Mario Guillermo; Errecalde, Marcelo Luis
Año de publicación
2005
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Clustering task aims at the unsupervised classification of patterns (e.g., observations, data, vec- tors, etc.) in different groups. Clustering problem has been approached from different disciplines during the last years. Although have been proposed different alternatives to cope with clustering, there also exists an interesting and novel field of research from which different bioinspired algorithms have emerged, e.g., genetic algorithms and ant colony algorithms. In this article we pro- pose an extension of the AntTree algorithm, an example of an algorithm recently proposed for a data mining task which is designed following the principle of self-assembling behavior observed in some species of real ants. The extension proposed called Adaptive-AntTree (AAT for short) represents a more flexible version of the original one. The ants in AAT are able of changing the assigned position in previous iterations in the tree under construction. As a consequence, this new algorithm builds an adaptive hierarchical cluster which changes over the run in order to improve the final result. The AAT performance is experimentally analyzed and compared against AntTree and K-means which is one of the more popular and referenced clustering algorithm.
Facultad de Informática
Materia
Ciencias Informáticas
Clustering
Data mining
computational intelligence
bioinspired algorithms
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9603

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network_name_str SEDICI (UNLP)
spelling Adaptive clustering with artificial antsIngaramo, Diego AlejandroLeguizamón, Mario GuillermoErrecalde, Marcelo LuisCiencias InformáticasClusteringData miningcomputational intelligencebioinspired algorithmsClustering task aims at the unsupervised classification of patterns (e.g., observations, data, vec- tors, etc.) in different groups. Clustering problem has been approached from different disciplines during the last years. Although have been proposed different alternatives to cope with clustering, there also exists an interesting and novel field of research from which different bioinspired algorithms have emerged, e.g., genetic algorithms and ant colony algorithms. In this article we pro- pose an extension of the AntTree algorithm, an example of an algorithm recently proposed for a data mining task which is designed following the principle of self-assembling behavior observed in some species of real ants. The extension proposed called Adaptive-AntTree (AAT for short) represents a more flexible version of the original one. The ants in AAT are able of changing the assigned position in previous iterations in the tree under construction. As a consequence, this new algorithm builds an adaptive hierarchical cluster which changes over the run in order to improve the final result. The AAT performance is experimentally analyzed and compared against AntTree and K-means which is one of the more popular and referenced clustering algorithm.Facultad de Informática2005-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf264-271http://sedici.unlp.edu.ar/handle/10915/9603enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec05-16.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:50:44Zoai:sedici.unlp.edu.ar:10915/9603Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:45.08SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Adaptive clustering with artificial ants
title Adaptive clustering with artificial ants
spellingShingle Adaptive clustering with artificial ants
Ingaramo, Diego Alejandro
Ciencias Informáticas
Clustering
Data mining
computational intelligence
bioinspired algorithms
title_short Adaptive clustering with artificial ants
title_full Adaptive clustering with artificial ants
title_fullStr Adaptive clustering with artificial ants
title_full_unstemmed Adaptive clustering with artificial ants
title_sort Adaptive clustering with artificial ants
dc.creator.none.fl_str_mv Ingaramo, Diego Alejandro
Leguizamón, Mario Guillermo
Errecalde, Marcelo Luis
author Ingaramo, Diego Alejandro
author_facet Ingaramo, Diego Alejandro
Leguizamón, Mario Guillermo
Errecalde, Marcelo Luis
author_role author
author2 Leguizamón, Mario Guillermo
Errecalde, Marcelo Luis
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Clustering
Data mining
computational intelligence
bioinspired algorithms
topic Ciencias Informáticas
Clustering
Data mining
computational intelligence
bioinspired algorithms
dc.description.none.fl_txt_mv Clustering task aims at the unsupervised classification of patterns (e.g., observations, data, vec- tors, etc.) in different groups. Clustering problem has been approached from different disciplines during the last years. Although have been proposed different alternatives to cope with clustering, there also exists an interesting and novel field of research from which different bioinspired algorithms have emerged, e.g., genetic algorithms and ant colony algorithms. In this article we pro- pose an extension of the AntTree algorithm, an example of an algorithm recently proposed for a data mining task which is designed following the principle of self-assembling behavior observed in some species of real ants. The extension proposed called Adaptive-AntTree (AAT for short) represents a more flexible version of the original one. The ants in AAT are able of changing the assigned position in previous iterations in the tree under construction. As a consequence, this new algorithm builds an adaptive hierarchical cluster which changes over the run in order to improve the final result. The AAT performance is experimentally analyzed and compared against AntTree and K-means which is one of the more popular and referenced clustering algorithm.
Facultad de Informática
description Clustering task aims at the unsupervised classification of patterns (e.g., observations, data, vec- tors, etc.) in different groups. Clustering problem has been approached from different disciplines during the last years. Although have been proposed different alternatives to cope with clustering, there also exists an interesting and novel field of research from which different bioinspired algorithms have emerged, e.g., genetic algorithms and ant colony algorithms. In this article we pro- pose an extension of the AntTree algorithm, an example of an algorithm recently proposed for a data mining task which is designed following the principle of self-assembling behavior observed in some species of real ants. The extension proposed called Adaptive-AntTree (AAT for short) represents a more flexible version of the original one. The ants in AAT are able of changing the assigned position in previous iterations in the tree under construction. As a consequence, this new algorithm builds an adaptive hierarchical cluster which changes over the run in order to improve the final result. The AAT performance is experimentally analyzed and compared against AntTree and K-means which is one of the more popular and referenced clustering algorithm.
publishDate 2005
dc.date.none.fl_str_mv 2005-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/9603
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dc.language.none.fl_str_mv eng
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info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
264-271
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instname:Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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