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Graph-Based Clustering and Data Visualization Algorithms [electronic resource] / by �Agnes Vathy-Fogarassy, J�anos Abonyi.

By: Vathy-Fogarassy, �Agnes [author.].
Contributor(s): Abonyi, J�anos [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: London : Springer London : Imprint: Springer, 2013Description: XIII, 110 p. 62 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781447151586.Subject(s): Computer science | Data mining | Mathematics | Visualization | Computer Science | Data Mining and Knowledge Discovery | VisualizationAdditional physical formats: Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
Vector Quantisation and Topology-Based Graph Representation -- Graph-Based Clustering Algorithms -- Graph-Based Visualisation of High-Dimensional Data.
In: Springer eBooksSummary: 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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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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