Taxonomic evidence and robustness of the classification applying intelligent data mining.

Numerical Taxonomy aims to group in families, using so-called structure analysis of operational taxonomic units (OTUs or taxons or taxa). Clusters that constitute families with a new criterion, is the purpose of this series of papers. Structural analysis, based on phenotypic characteristics, exhib...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Perichinsky, Gregorio, Servente, Magdalena, Servetto, Arturo Carlos, García Martínez, Ramón, Orellana, Rosa Beatriz, Plastino, Ángel Luis
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2003
Materias:
Ciencias Informáticas
Data mining
Entropía
Applications
ARTIFICIAL INTELLIGENCE
classification
cluster (family)
spectrum
induction
divide and rule
entropy
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/22870
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-22870
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
Data mining
Entropía
Applications
ARTIFICIAL INTELLIGENCE
classification
cluster (family)
spectrum
induction
divide and rule
entropy
spellingShingle Ciencias Informáticas
Data mining
Entropía
Applications
ARTIFICIAL INTELLIGENCE
classification
cluster (family)
spectrum
induction
divide and rule
entropy
Perichinsky, Gregorio
Servente, Magdalena
Servetto, Arturo Carlos
García Martínez, Ramón
Orellana, Rosa Beatriz
Plastino, Ángel Luis
Taxonomic evidence and robustness of the classification applying intelligent data mining.
topic_facet Ciencias Informáticas
Data mining
Entropía
Applications
ARTIFICIAL INTELLIGENCE
classification
cluster (family)
spectrum
induction
divide and rule
entropy
description Numerical Taxonomy aims to group in families, using so-called structure analysis of operational taxonomic units (OTUs or taxons or taxa). Clusters that constitute families with a new criterion, is the purpose of this series of papers. Structural analysis, based on phenotypic characteristics, exhibits the relationships, in terms of degrees of similarity, through the computation of the Matrix of Similarity, applying the technique of integration dynamic of independent domains, of the semantics of the Dynamic Relational Database Model. The main contribution is to introduce the concept of spectrum of the OTUs, based in the states of their characters. The concept of families' spectra emerges, if the principles of superposition and interference, and the Invariants (centroid, variance and radius) determined by the maximum of the Bienaymé-Tchebycheff relation, are applied to the spectra of the OTUs. Using in successive form an updated database through the increase of the cardinal of the tuples, and as the resulting families are the same, we ascertain the robustness of the method. Through Intelligent Data Mining, we focused our interest on the Quinlan algorithms, applied in classification problems with the Gain of Entropy, we contrast the Computational Taxonomy, obtaining a new criterion of the robustness of the method.
format Objeto de conferencia
Objeto de conferencia
author Perichinsky, Gregorio
Servente, Magdalena
Servetto, Arturo Carlos
García Martínez, Ramón
Orellana, Rosa Beatriz
Plastino, Ángel Luis
author_facet Perichinsky, Gregorio
Servente, Magdalena
Servetto, Arturo Carlos
García Martínez, Ramón
Orellana, Rosa Beatriz
Plastino, Ángel Luis
author_sort Perichinsky, Gregorio
title Taxonomic evidence and robustness of the classification applying intelligent data mining.
title_short Taxonomic evidence and robustness of the classification applying intelligent data mining.
title_full Taxonomic evidence and robustness of the classification applying intelligent data mining.
title_fullStr Taxonomic evidence and robustness of the classification applying intelligent data mining.
title_full_unstemmed Taxonomic evidence and robustness of the classification applying intelligent data mining.
title_sort taxonomic evidence and robustness of the classification applying intelligent data mining.
publishDate 2003
url http://sedici.unlp.edu.ar/handle/10915/22870
work_keys_str_mv AT perichinskygregorio taxonomicevidenceandrobustnessoftheclassificationapplyingintelligentdatamining
AT serventemagdalena taxonomicevidenceandrobustnessoftheclassificationapplyingintelligentdatamining
AT servettoarturocarlos taxonomicevidenceandrobustnessoftheclassificationapplyingintelligentdatamining
AT garciamartinezramon taxonomicevidenceandrobustnessoftheclassificationapplyingintelligentdatamining
AT orellanarosabeatriz taxonomicevidenceandrobustnessoftheclassificationapplyingintelligentdatamining
AT plastinoangelluis taxonomicevidenceandrobustnessoftheclassificationapplyingintelligentdatamining
bdutipo_str Repositorios
_version_ 1764820467865092098

Ejemplares similares