Graph-Based Clustering and Data Visualization Algorithms
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.
1114976994
Graph-Based Clustering and Data Visualization Algorithms
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.
64.99 In Stock
Graph-Based Clustering and Data Visualization Algorithms

Graph-Based Clustering and Data Visualization Algorithms

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

Graph-Based Clustering and Data Visualization Algorithms

by Ágnes Vathy-Fogarassy, János Abonyi

Paperback(2013)

$64.99 
  • SHIP THIS ITEM
    In stock. Ships in 1-2 days.
  • PICK UP IN STORE

    Your local store may have stock of this item.

Related collections and offers


Overview

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.

Product Details

ISBN-13: 9781447151579
Publisher: Springer London
Publication date: 05/24/2013
Series: SpringerBriefs in Computer Science
Edition description: 2013
Pages: 110
Product dimensions: 6.10(w) x 9.25(h) x 0.01(d)

Table of Contents

Vector Quantisation and Topology-Based Graph Representation

Graph-Based Clustering Algorithms

Graph-Based Visualisation of High-Dimensional Data

From the B&N Reads Blog

Customer Reviews