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Book Cover
Author Vathy-Fogarassy, Ágnes, author

Title Graph-based clustering and data visualization algorithms / Ágnes Vathy-Fogarassy, János Abonyi
Published London : Springer, [2013]
Online access available from:
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Description 1 online resource (xiii, 110 pages) : illustrations
Series SpringerBriefs in computer science, 2191-5768
SpringerBriefs in computer science.
Contents Vector Quantisation and Topology Based Graph Representation -- Graph-Based Clustering Algorithms -- Graph-Based Visualisation of High Dimensional Data
Summary 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
Analysis Computer science
Data mining
Bibliography Includes bibliographical references and index
Notes Print version record
Subject Data mining.
Cluster analysis -- Data processing.
Graph algorithms.
Electronic Data Processing.
Cluster Analysis.
Data Mining.
Form Electronic book
Author Abonyi, Janos, 1974- author
ISBN 9781447151586 (electronic bk.)
1447151585 (electronic bk.)