Articles liés à Graph-Based Clustering and Data Visualization Algorithms

Graph-Based Clustering and Data Visualization Algorithms - Couverture souple

Livre 72 sur 322: SpringerBriefs in Computer Science

Vathy-fogarassy, gnes; Abonyi, Jnos

 
9781447151579: Graph-Based Clustering and Data Visualization Algorithms
Couverture souple
ISBN 10 : 1447151577 ISBN 13 : 9781447151579
Editeur : Springer, 2013

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l'auteur

Dr. János Abonyi is a Full Professor of Computer Science and Chemical Engineering at the Department of Process Engineering, University of Pannonia, Veszprém, Hungary. His other publications include the Springer titles Interpretability of Computational Intelligence-Based Regression Models, and (with Dr. Vathy-Fogarassy) Graph-Based Clustering and Data Visualization Algorithms. Dr. Ágnes Vathy-Fogarassy is an Associate Professor at the Department of Computer Science and Systems Technology at the University of Pannonia. Dániel Leitold is an Assistant Lecturer at the University of Pannonia.

À propos de la quatrième de couverture

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Springer
Date d'édition
2013
Langue
anglais
ISBN 10 
1447151577
ISBN 13 
9781447151579
Reliure
Broché
Nombre de pages
110
Coordonnées du fabricant
non disponible
Personne responsable
non disponible