Graph-Based Clustering and Data Visualization Algorithms (SpringerBriefs in Computer Science) - Tapa blanda

Libro 72 de 322: SpringerBriefs in Computer Science

Vathy-Fogarassy, Ágnes; Abonyi, János

 
9781447151579: Graph-Based Clustering and Data Visualization Algorithms (SpringerBriefs in Computer Science)
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ISBN 10: 1447151577 ISBN 13: 9781447151579
Editorial: Springer, 2013

Sinopsis

This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website.

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De la contraportada

This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2013
Idioma
Inglés
ISBN 10 
1447151577
ISBN 13 
9781447151579
Encuadernación
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Número de páginas
124
Contacto del fabricante
no disponible
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9781447151593: Graph-Based Clustering and Data Visualization Algorithms

Edición Destacada

ISBN 10:  1447151593 ISBN 13:  9781447151593
Editorial: Springer, 2013
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