Overview
- Examines vector quantization methods, and discusses the advantages and disadvantages of minimal spanning tree-based clustering
- Presents a novel similarity measure to improve the classical Jarvis-Patrick clustering algorithm
- Reviews distance-, neighborhood- and topology-based dimensionality reduction methods, and introduces new graph-based visualization algorithms
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
About this book
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.
Similar content being viewed by others
Table of contents (3 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Graph-Based Clustering and Data Visualization Algorithms
Authors: Ágnes Vathy-Fogarassy, János Abonyi
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-1-4471-5158-6
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: János Abonyi 2013
Softcover ISBN: 978-1-4471-5157-9Published: 05 June 2013
eBook ISBN: 978-1-4471-5158-6Published: 24 May 2013
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XIII, 110
Number of Illustrations: 62 b/w illustrations