Graph-Based Clustering and Data Visualization Algorithms (SpringerBriefs in Computer Science)

Vathy-Fogarassy, Ágnes; Abonyi, János

ISBN 10: 1447151577 / ISBN 13: 9781447151579

Verlag: Springer, 2013

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Bibliografische Details

Titel: Graph-Based Clustering and Data ...

Verlag: Springer

Erscheinungsdatum: 2013

Einband: paperback

Zustand: Good

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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.