Graph-Based Clustering and Data Visualization Algorithms

✍ Scribed by Ágnes Vathy-Fogarassy, János Abonyi (auth.)

Publisher: Springer-Verlag London
Year: 2013
Tongue: English
Leaves: 120
Series: SpringerBriefs in Computer Science
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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 Matter....Pages i-xiii
Vector Quantisation and Topology Based Graph Representation....Pages 1-16
Graph-Based Clustering Algorithms....Pages 17-41
Graph-Based Visualisation of High Dimensional Data....Pages 43-91
Back Matter....Pages 93-110

✦ Subjects

Data Mining and Knowledge Discovery; Visualization

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