Open AccessTaxonomic Evidence Applying Intelligent Information
Author(s) -
Gregorio Perichinsky,
Elizabeth Miriam Jiménez Rey,
María Delia Grossi,
Félix Anibal Vallejos,
Arturo Carlos Servetto,
R. B. Orellana,
A. Plastino
Publication year - 2009
Publication title -
revista eletrônica de sistemas de informação
Language(s) - English
Resource type - Journals
ISSN - 1677-3071
DOI - 10.21529/resi.2005.0402006
Subject(s) - taxonomy (biology) , euclidean distance , computer science , entropy (arrow of time) , similarity (geometry) , taxonomic rank , euclidean geometry , data mining , artificial intelligence , taxon , pattern recognition (psychology) , machine learning , mathematics , biology , ecology , physics , geometry , quantum mechanics , image (mathematics)
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.