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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
EUR 9,57 expédition depuis Etats-Unis vers France
Destinations, frais et délaisEUR 9,70 expédition depuis Allemagne vers France
Destinations, frais et délaisVendeur : BooksRun, Philadelphia, PA, Etats-Unis
Paperback. Etat : Good. 2013. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported. N° de réf. du vendeur 1447151577-11-1
Quantité disponible : 1 disponible(s)
Vendeur : moluna, Greven, Allemagne
Kartoniert / Broschiert. Etat : 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 algorithmReviews distance-,. N° de réf. du vendeur 4185173
Quantité disponible : Plus de 20 disponibles
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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 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. 124 pp. Englisch. N° de réf. du vendeur 9781447151579
Quantité disponible : 2 disponible(s)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Etat : New. In. N° de réf. du vendeur ria9781447151579_new
Quantité disponible : Plus de 20 disponibles
Vendeur : Chiron Media, Wallingford, Royaume-Uni
PF. Etat : New. N° de réf. du vendeur 6666-IUK-9781447151579
Quantité disponible : 10 disponible(s)
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : 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 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. N° de réf. du vendeur 9781447151579
Quantité disponible : 1 disponible(s)
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. 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 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 124 pp. Englisch. N° de réf. du vendeur 9781447151579
Quantité disponible : 2 disponible(s)
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Etat : New. N° de réf. du vendeur 19738156-n
Quantité disponible : Plus de 20 disponibles
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Etat : New. N° de réf. du vendeur 19738156-n
Quantité disponible : Plus de 20 disponibles
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 234. N° de réf. du vendeur C9781447151579
Quantité disponible : Plus de 20 disponibles