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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/93730
Ver los metadatos del registro completo
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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 http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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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 |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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