Graph-Based Clustering and Data Visualization Algorithms by Vathy-Fogarassy, Ágnes; Abonyi, János;

Series: SpringerBriefs in Computer Science;

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Product details:

Edition number 2013
Publisher Springer London
Date of Publication 5 June 2013
Number of Volumes 1 pieces, Book

ISBN 9781447151579
Binding Paperback
No. of pages110 pages
Size 235x155 mm
Weight 454 g
Language English
Illustrations XIII, 110 p. 62 illus. Illustrations, black & white

Categories

Geometry and topology Combinatorics and graph theory Probability and mathematical statistics System analysis, system planning Data management in computer systems Database management softwares Artificial Intelligence Further readings in the field of computing Geometry and topology (charity campaign) Combinatorics and graph theory (charity campaign) Probability and mathematical statistics (charity campaign) System analysis, system planning (charity campaign) Data management in computer systems (charity campaign) Database management softwares (charity campaign) Artificial Intelligence (charity campaign) Further readings in the field of computing (charity campaign)

Long 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.