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008 150903s2013 xxk| o |||| 0|eng d
020 _a9781447151586
_99781447151586
024 7 _a10.1007/9781447151586
_2doi
035 _avtls000340025
039 9 _a201509030321
_bVLOAD
_c201404300408
_dVLOAD
_y201402061015
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA76.9.D343
100 1 _aVathy-Fogarassy, Ágnes.
_eautor
_9315047
245 1 0 _aGraph-Based Clustering and Data Visualization Algorithms /
_cby Ágnes Vathy-Fogarassy, János Abonyi.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2013.
300 _axiii, 110 páginas 62 ilustraciones
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aSpringerBriefs in Computer Science,
_x2191-5768
500 _aSpringer eBooks
505 0 _aVector Quantisation and Topology-Based Graph Representation -- Graph-Based Clustering Algorithms -- Graph-Based Visualisation of High-Dimensional Data.
520 _aThis 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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aAbonyi, János.
_eautor
_9315048
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781447151579
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-5158-6
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c286437
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