Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families

Autores
Perichinsky, Gregorio; Jiménez Rey, Elizabeth Miriam; Grossi, María Delia; Vallejos, Félix Anibal; Servetto, Arturo Carlos; Orellana, Rosa Beatriz; Plastino, Ángel Luis
Año de publicación
2005
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The Numeric Taxonomy aims to group operational taxonomic units in clusters (OTUs or taxons or taxa), using the denominated structure analysis by means of numeric methods. These clusters that constitute families are the purpose of this series of projects and they emerge of the structural analysis, of their phenotypical characteristic, exhibiting the relationships in terms of grades of similarity of the OTUs, employing tools such as i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix and in this way the significant concept of spectrum of the OTUs is introduced, being based the same one on the state of their characters. A new taxonomic criterion is thereby formulated and a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining, when apply of Machine Learning techniques, in particular to the C4.5 algorithms, created by Quinlan, the degree of efficiency achieved by the TDIDT family´s algorithms when are generating valid models of the data in classification problems with the Gain of Entropy through Maximum Entropy Principle.
Facultad de Ciencias Astronómicas y Geofísicas
Facultad de Ciencias Exactas
Materia
Física
Ciencias Informáticas
Asteroids
Numeric taxonomy
Intelligent information algorithms
Entrophy
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/93730

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network_name_str SEDICI (UNLP)
spelling Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids familiesPerichinsky, GregorioJiménez Rey, Elizabeth MiriamGrossi, María DeliaVallejos, Félix AnibalServetto, Arturo CarlosOrellana, Rosa BeatrizPlastino, Ángel LuisFísicaCiencias InformáticasAsteroidsNumeric taxonomyIntelligent information algorithmsEntrophyThe Numeric Taxonomy aims to group operational taxonomic units in clusters (OTUs or taxons or taxa), using the denominated structure analysis by means of numeric methods. These clusters that constitute families are the purpose of this series of projects and they emerge of the structural analysis, of their phenotypical characteristic, exhibiting the relationships in terms of grades of similarity of the OTUs, employing tools such as i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix and in this way the significant concept of spectrum of the OTUs is introduced, being based the same one on the state of their characters. A new taxonomic criterion is thereby formulated and a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining, when apply of Machine Learning techniques, in particular to the C4.5 algorithms, created by Quinlan, the degree of efficiency achieved by the TDIDT family´s algorithms when are generating valid models of the data in classification problems with the Gain of Entropy through Maximum Entropy Principle.Facultad de Ciencias Astronómicas y GeofísicasFacultad de Ciencias Exactas2005-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf1-14http://sedici.unlp.edu.ar/handle/10915/93730enginfo:eu-repo/semantics/altIdentifier/url/http://www.periodicosibepes.org.br/index.php/reinfo/article/view/160info:eu-repo/semantics/altIdentifier/issn/1677-3071info:eu-repo/semantics/altIdentifier/doi/10.21529/RESI.2005.0402006info:eu-repo/semantics/altIdentifier/hdl/11336/39609info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:19:26Zoai:sedici.unlp.edu.ar:10915/93730Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:19:27.167SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families
title Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families
spellingShingle Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families
Perichinsky, Gregorio
Física
Ciencias Informáticas
Asteroids
Numeric taxonomy
Intelligent information algorithms
Entrophy
title_short Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families
title_full Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families
title_fullStr Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families
title_full_unstemmed Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families
title_sort Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families
dc.creator.none.fl_str_mv Perichinsky, Gregorio
Jiménez Rey, Elizabeth Miriam
Grossi, María Delia
Vallejos, Félix Anibal
Servetto, Arturo Carlos
Orellana, Rosa Beatriz
Plastino, Ángel Luis
author Perichinsky, Gregorio
author_facet Perichinsky, Gregorio
Jiménez Rey, Elizabeth Miriam
Grossi, María Delia
Vallejos, Félix Anibal
Servetto, Arturo Carlos
Orellana, Rosa Beatriz
Plastino, Ángel Luis
author_role author
author2 Jiménez Rey, Elizabeth Miriam
Grossi, María Delia
Vallejos, Félix Anibal
Servetto, Arturo Carlos
Orellana, Rosa Beatriz
Plastino, Ángel Luis
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Física
Ciencias Informáticas
Asteroids
Numeric taxonomy
Intelligent information algorithms
Entrophy
topic Física
Ciencias Informáticas
Asteroids
Numeric taxonomy
Intelligent information algorithms
Entrophy
dc.description.none.fl_txt_mv The Numeric Taxonomy aims to group operational taxonomic units in clusters (OTUs or taxons or taxa), using the denominated structure analysis by means of numeric methods. These clusters that constitute families are the purpose of this series of projects and they emerge of the structural analysis, of their phenotypical characteristic, exhibiting the relationships in terms of grades of similarity of the OTUs, employing tools such as i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix and in this way the significant concept of spectrum of the OTUs is introduced, being based the same one on the state of their characters. A new taxonomic criterion is thereby formulated and a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining, when apply of Machine Learning techniques, in particular to the C4.5 algorithms, created by Quinlan, the degree of efficiency achieved by the TDIDT family´s algorithms when are generating valid models of the data in classification problems with the Gain of Entropy through Maximum Entropy Principle.
Facultad de Ciencias Astronómicas y Geofísicas
Facultad de Ciencias Exactas
description The Numeric Taxonomy aims to group operational taxonomic units in clusters (OTUs or taxons or taxa), using the denominated structure analysis by means of numeric methods. These clusters that constitute families are the purpose of this series of projects and they emerge of the structural analysis, of their phenotypical characteristic, exhibiting the relationships in terms of grades of similarity of the OTUs, employing tools such as i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix and in this way the significant concept of spectrum of the OTUs is introduced, being based the same one on the state of their characters. A new taxonomic criterion is thereby formulated and a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining, when apply of Machine Learning techniques, in particular to the C4.5 algorithms, created by Quinlan, the degree of efficiency achieved by the TDIDT family´s algorithms when are generating valid models of the data in classification problems with the Gain of Entropy through Maximum Entropy Principle.
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
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info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/93730
url http://sedici.unlp.edu.ar/handle/10915/93730
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.periodicosibepes.org.br/index.php/reinfo/article/view/160
info:eu-repo/semantics/altIdentifier/issn/1677-3071
info:eu-repo/semantics/altIdentifier/doi/10.21529/RESI.2005.0402006
info:eu-repo/semantics/altIdentifier/hdl/11336/39609
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
1-14
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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