ENGLISH

Graph-Based Clustering and Data Visualization Algorithms

By János Abonyi (auth.) , Ágnes Vathy-Fogarassy

Book information

Publisher
Springer-Verlag London
Year
2013
ISBN
978-1-4471-5157-9, 978-1-4471-5158-6
DOI
10.1007/978-1-4471-5158-6
Language
english
Format
PDF
Filesize
4 MB (4000957 bytes)
Series
SpringerBriefs in Computer Science
Edition
1
Pages
110\120
Orientation
yes
Scanned
yes
Time added
2013-08-01 04:00:00
Data Mining and Knowledge Discovery; Visualization

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.

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