Téléchargez le livre :  Graph-Based Clustering and Data Visualization Algorithms
0%

János Abonyi , Ágnes Vathy-Fogarassy

Graph-Based Clustering and Data Visualization Algorithms

Springer

Collection : SpringerBriefs in Computer Science

Date de publication : 2013-05-24



58,01
Guide des formats
Description
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.
Pages
110
Collection
SpringerBriefs in Computer Science
Parution
2013-05-24
Marque
Springer
EAN papier
9781447151579
EAN EPUB
9781447151586

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
11
Taille du fichier
2621 Ko
Prix
58,01 €