Parallelizing a new environment based clustering method

There exists a wide range of problems which requires the automatic classification of a data set. In this sense, clustering techniques have been applied, since they are characterized by forming classes or groups using a predefined similarity measure. The present article presents algorithm architectu...

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Detalles Bibliográficos
Autores principales:	Lanzarini, Laura Cristina, De Giusti, Armando Eduardo
Formato:	Objeto de conferencia
Lenguaje:	Inglés
Publicado:	2001
Materias:	Ciencias Informáticas Parallel Environments Clustering Algorithms Concurrent Programming Parallel Algorithms Clustering Techniques Image Segmentation Classification
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/23315
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-23315
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 Parallel Environments Clustering Algorithms Concurrent Programming Parallel Algorithms Clustering Techniques Image Segmentation Classification
spellingShingle	Ciencias Informáticas Parallel Environments Clustering Algorithms Concurrent Programming Parallel Algorithms Clustering Techniques Image Segmentation Classification Lanzarini, Laura Cristina De Giusti, Armando Eduardo Parallelizing a new environment based clustering method
topic_facet	Ciencias Informáticas Parallel Environments Clustering Algorithms Concurrent Programming Parallel Algorithms Clustering Techniques Image Segmentation Classification
description	There exists a wide range of problems which requires the automatic classification of a data set. In this sense, clustering techniques have been applied, since they are characterized by forming classes or groups using a predefined similarity measure. The present article presents algorithm architecture and structure for paralleling clustering algorithm EBC (environment based clustering) which, deferring from usual solutions, processes input patterns in order to establish the similarity measure to be used. Results obtained are analyzed over images of liver tissues with a maximum range of 256 colors, studying algorithm dependence on image resolutions and the number of different patterns in them. Then, critical points of the sequential algorithm are optimized over a PC net architecture. Finally, the extension of the results obtained are discussed, as well as the solution presented for the case of high resolution images, in which the number of different patterns is of higher order (between 3000 and 5000).
format	Objeto de conferencia Objeto de conferencia
author	Lanzarini, Laura Cristina De Giusti, Armando Eduardo
author_facet	Lanzarini, Laura Cristina De Giusti, Armando Eduardo
author_sort	Lanzarini, Laura Cristina
title	Parallelizing a new environment based clustering method
title_short	Parallelizing a new environment based clustering method
title_full	Parallelizing a new environment based clustering method
title_fullStr	Parallelizing a new environment based clustering method
title_full_unstemmed	Parallelizing a new environment based clustering method
title_sort	parallelizing a new environment based clustering method
publishDate	2001
url	http://sedici.unlp.edu.ar/handle/10915/23315
work_keys_str_mv	AT lanzarinilauracristina parallelizinganewenvironmentbasedclusteringmethod AT degiustiarmandoeduardo parallelizinganewenvironmentbasedclusteringmethod
bdutipo_str	Repositorios
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Parallelizing a new environment based clustering method

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