Graph-Based Clustering and Data Visualization Algorithms (Paperback, 2013 ed.)

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.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	SpringerBriefs in Computer Science
Release date:	June 2013
First published:	2013
Authors:	Agnes Vathy-Fogarassy • Janos Abonyi
Dimensions:	235 x 155 x 6mm (L x W x T)
Format:	Paperback
Pages:	110
Edition:	2013 ed.
ISBN-13:	978-1-4471-5157-9
Categories:	Books > Science & Mathematics > Mathematics > Combinatorics & graph theory Books > Computing & IT > Applications of computing > Databases > Data mining
LSN:	1-4471-5157-7
Barcode:	9781447151579

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