Items related to Graph-Based Clustering and Data Visualization Algorithms...

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

Book 72 of 322: SpringerBriefs in Computer Science

Vathy-Fogarassy, Ágnes; Abonyi, János

 
9781447151579: Graph-Based Clustering and Data Visualization Algorithms (SpringerBriefs in Computer Science)
Softcover
ISBN 10: 1447151577 ISBN 13: 9781447151579
Publisher: 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.

"synopsis" may belong to another edition of this title.

From the Back Cover

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.

"About this title" may belong to another edition of this title.

Publisher
Springer
Publication date
2013
Language
English
ISBN 10 
1447151577
ISBN 13 
9781447151579
Binding
Paperback
Number of pages
123