Graph-Based Clustering and Data Visualization Algorithms

Nonfiction, Science & Nature, Mathematics, Graphic Methods, Computers, Database Management, General Computing
Cover of the book Graph-Based Clustering and Data Visualization Algorithms by Ágnes Vathy-Fogarassy, János Abonyi, Springer London
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Author: Ágnes Vathy-Fogarassy, János Abonyi ISBN: 9781447151586
Publisher: Springer London Publication: May 24, 2013
Imprint: Springer Language: English
Author: Ágnes Vathy-Fogarassy, János Abonyi
ISBN: 9781447151586
Publisher: Springer London
Publication: May 24, 2013
Imprint: Springer
Language: English

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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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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