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.
Fil: Perichinsky, Gregorio. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Jiménez Rey, Elizabeth Miriam. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Grossi, María Delia. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Vallejos, Félix Anibal. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Fil: Servetto, Arturo Carlos. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Orellana, Rosa Beatriz. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Plastino, Ángel Luis. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina
Materia
Asteroids
Numeric taxonomy
Intelligent information algorithms
Entrophy
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/39609
Acceder
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network_name_str CONICET Digital (CONICET)
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 LuisAsteroidsNumeric taxonomyIntelligent information algorithmsEntrophyhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1The 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.Fil: Perichinsky, Gregorio. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Jiménez Rey, Elizabeth Miriam. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Grossi, María Delia. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Vallejos, Félix Anibal. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Servetto, Arturo Carlos. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Orellana, Rosa Beatriz. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Plastino, Ángel Luis. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; ArgentinaFacultade Cenecista de Campo Largo2005-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/39609Perichinsky, Gregorio; Jiménez Rey, Elizabeth Miriam; Grossi, María Delia; Vallejos, Félix Anibal; Servetto, Arturo Carlos; et al.; Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families; Facultade Cenecista de Campo Largo; Revista Electrônica de Sistemas de Informacao; 4; 2; 12-2005; 1-141677-3071CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.21529/RESI.2005.0402006info:eu-repo/semantics/altIdentifier/url/http://www.periodicosibepes.org.br/index.php/reinfo/article/view/160info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:34:42Zoai:ri.conicet.gov.ar:11336/39609instacron: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:34982025-09-29 09:34:42.779CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
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 Asteroids
Numeric taxonomy
Intelligent information algorithms
Entrophy
topic Asteroids
Numeric taxonomy
Intelligent information algorithms
Entrophy
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
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.
Fil: Perichinsky, Gregorio. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Jiménez Rey, Elizabeth Miriam. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Grossi, María Delia. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Vallejos, Félix Anibal. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Fil: Servetto, Arturo Carlos. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Orellana, Rosa Beatriz. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Plastino, Ángel Luis. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina
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
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/11336/39609
Perichinsky, Gregorio; Jiménez Rey, Elizabeth Miriam; Grossi, María Delia; Vallejos, Félix Anibal; Servetto, Arturo Carlos; et al.; Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families; Facultade Cenecista de Campo Largo; Revista Electrônica de Sistemas de Informacao; 4; 2; 12-2005; 1-14
1677-3071
CONICET Digital
CONICET
url http://hdl.handle.net/11336/39609
identifier_str_mv Perichinsky, Gregorio; Jiménez Rey, Elizabeth Miriam; Grossi, María Delia; Vallejos, Félix Anibal; Servetto, Arturo Carlos; et al.; Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families; Facultade Cenecista de Campo Largo; Revista Electrônica de Sistemas de Informacao; 4; 2; 12-2005; 1-14
1677-3071
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.21529/RESI.2005.0402006
info:eu-repo/semantics/altIdentifier/url/http://www.periodicosibepes.org.br/index.php/reinfo/article/view/160
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Facultade Cenecista de Campo Largo
publisher.none.fl_str_mv Facultade Cenecista de Campo Largo
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str 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
_version_ 1844613075937787904
score 13.070432

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