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Autore principale: Diana Porro Muñoz
Natura: Artículo científico
Lingua:en
Pubblicazione: Instituto Politécnico Nacional 2011
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Accesso online:https://www.redalyc.org/articulo.oa?id=61520862011
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author Diana Porro Muñoz
author_facet Diana Porro Muñoz
contents Combining Dissimilarities for Three-Way Data Classification Diana Porro Muñoz Isneri Talavera Robert P. W. Duin Mauricio Orozco Alzate Computación three way data Classification combination and dissimilarity representation The representation of objects by multidimensional arrays is widely applied in many research areas. Nevertheless, there is a lack of tools to classify data with this structure. In this paper, an approach for classifying objects represented by matrices is introduced, based on the advantages and success of the combination strategy, and particularly in the dissimilarity representation. A procedure for obtaining the new representation of the data has also been developed, aimed at obtaining a more powerful representation. The proposed approach is evaluated on two threeway data sets. This has been done by comparing the different ways of achieving the new representation, and the traditional vector representation of the objects. 2011 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61520862011 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.1 Vol.15
format Artículo científico
id redalyc_61520862011
language en
publishDate 2011
publisher Instituto Politécnico Nacional
spellingShingle Combining Dissimilarities for Three-Way Data Classification
Diana Porro Muñoz
Computación
three
way data
Classification
combination and dissimilarity representation
Combining Dissimilarities for Three-Way Data Classification Diana Porro Muñoz Isneri Talavera Robert P. W. Duin Mauricio Orozco Alzate Computación three way data Classification combination and dissimilarity representation The representation of objects by multidimensional arrays is widely applied in many research areas. Nevertheless, there is a lack of tools to classify data with this structure. In this paper, an approach for classifying objects represented by matrices is introduced, based on the advantages and success of the combination strategy, and particularly in the dissimilarity representation. A procedure for obtaining the new representation of the data has also been developed, aimed at obtaining a more powerful representation. The proposed approach is evaluated on two threeway data sets. This has been done by comparing the different ways of achieving the new representation, and the traditional vector representation of the objects. 2011 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61520862011 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.1 Vol.15
title Combining Dissimilarities for Three-Way Data Classification
topic Computación
three
way data
Classification
combination and dissimilarity representation
url https://www.redalyc.org/articulo.oa?id=61520862011