Adaptive clustering with artificial ants

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, th...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Ingaramo, Diego Alejandro, Leguizamón, Mario Guillermo, Errecalde, Marcelo Luis
Formato: Articulo
Lenguaje:Inglés
Publicado: 2005
Materias:
Ciencias Informáticas
Clustering
Data mining
computational intelligence
bioinspired algorithms
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/9603
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec05-16.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-9603
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Clustering
Data mining
computational intelligence
bioinspired algorithms
spellingShingle Ciencias Informáticas
Clustering
Data mining
computational intelligence
bioinspired algorithms
Ingaramo, Diego Alejandro
Leguizamón, Mario Guillermo
Errecalde, Marcelo Luis
Adaptive clustering with artificial ants
topic_facet Ciencias Informáticas
Clustering
Data mining
computational intelligence
bioinspired algorithms
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.
format Articulo
Articulo
author Ingaramo, Diego Alejandro
Leguizamón, Mario Guillermo
Errecalde, Marcelo Luis
author_facet Ingaramo, Diego Alejandro
Leguizamón, Mario Guillermo
Errecalde, Marcelo Luis
author_sort Ingaramo, Diego Alejandro
title Adaptive clustering with artificial ants
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
publishDate 2005
url http://sedici.unlp.edu.ar/handle/10915/9603
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec05-16.pdf
work_keys_str_mv AT ingaramodiegoalejandro adaptiveclusteringwithartificialants
AT leguizamonmarioguillermo adaptiveclusteringwithartificialants
AT errecaldemarceloluis adaptiveclusteringwithartificialants
bdutipo_str Repositorios
_version_ 1764820491593318402

Ejemplares similares