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Graph-Based Clustering and Data Visualization Algorithms

by Vathy-Fogarassy, Ágnes.
Authors: Abonyi, János.%author. | SpringerLink (Online service) Series: SpringerBriefs in Computer Science, 2191-5768 Physical details: XIII, 110 p. 62 illus. online resource. ISBN: 1447151585 Subject(s): Computer science. | Data mining. | Visualization. | Computer Science. | Data Mining and Knowledge Discovery. | Visualization.
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E-Book E-Book AUM Main Library 006.312 (Browse Shelf) Not for loan

Vector Quantisation and Topology-Based Graph Representation -- Graph-Based Clustering Algorithms -- Graph-Based Visualisation of High-Dimensional Data.

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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