Graph-Based Clustering and Data Visualization Algorithms

Name: Graph-Based Clustering and Data Visualization Algorithms
SKU: 21300796
Price: 158.99 AUD
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Paperback / softback

Series:

Springerbriefs in Computer Science

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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.

Release date Australia

June 5th, 2013

Author

Audience

Professional & Vocational

Illustrations

62 Illustrations, black and white; XIII, 110 p. 62 illus.

Pages

110

Series

Springerbriefs in Computer Science

Dimensions

156x234x6

ISBN-13

9781447151579

Product ID

21300796

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