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Graph-Based Clustering and Data Visualization Algorithms

Ágnes Vathy-Fogarassy, János Abonyi (Autoren)

Buch | Softcover

110 Seiten

2013
Springer London Ltd (Verlag)
978-1-4471-5157-9 (ISBN)

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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.

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

Erscheint lt. Verlag	5.6.2013
Reihe/Serie	SpringerBriefs in Computer Science
Zusatzinfo	62 Illustrations, black and white; XIII, 110 p. 62 illus.
Verlagsort	England
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
	Informatik ► Software Entwicklung ► User Interfaces (HCI)
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Mathematik ► Graphentheorie
ISBN-10	1-4471-5157-7 / 1447151577
ISBN-13	978-1-4471-5157-9 / 9781447151579
Zustand	Neuware