eBook Graph-Based Clustering and Data Visualization Algorithms, 1st Edition

  • Published By:
  • ISBN-10: 1447151585
  • ISBN-13: 9781447151586
  • DDC: 006.312
  • Grade Level Range: College Freshman - College Senior
  • 110 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013
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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.

Table of Contents

Front Cover.
Other Frontmatter.
Title Page.
Copyright Page.
1: Vector Quantisation and Topology Based Graph Representation.
2: Graph-Based Clustering Algorithms.
3: Graph-Based Visualisation of High Dimensional Data.
Appendix: Constructing a Minimum Spanning Tree.