Graph Based Clustering Data Visualization by Vathy Fogarassy Ágnes (12 results)
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 clust…ering 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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Taschenbuch. Condition: Neu. Graph-Based Clustering and Data Visualization Algorithms | Ágnes Vathy-Fogarassy (u. a.) | Taschenbuch | SpringerBriefs in Computer Science | xiii | Englisch | 2013 | Springer | EAN 9781447151579 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[d…ot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Examines vector quantization methods, and discusses the advantages and disadvantages of minimal spanning tree-based clusteringPresents a novel similarity measure to improve the classical Jarvis-Patrick clustering algo…rithmReviews distance-..
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 gra…phs 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.Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg 124 pp. Englisch.
