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03215nam a22005175i 4500 |
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978-1-4471-5158-6 |
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20210617195106.0 |
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130525s2013 xxk| s |||| 0|eng d |
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|a 9781447151586
|9 978-1-4471-5158-6
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|a 10.1007/978-1-4471-5158-6
|2 doi
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|a 006.312
|2 23
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|a Vathy-Fogarassy, Ágnes.
|e author.
|0 (orcid)0000-0002-5524-1675
|1 https://orcid.org/0000-0002-5524-1675
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 245 |
1 |
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|a Graph-Based Clustering and Data Visualization Algorithms
|h [electronic resource] /
|c by Ágnes Vathy-Fogarassy, János Abonyi.
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| 250 |
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|a 1st ed. 2013.
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| 264 |
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1 |
|a London :
|b Springer London :
|b Imprint: Springer,
|c 2013.
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| 300 |
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|a XIII, 110 p. 62 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a SpringerBriefs in Computer Science,
|x 2191-5768
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| 505 |
0 |
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|a Vector Quantisation and Topology-Based Graph Representation -- Graph-Based Clustering Algorithms -- Graph-Based Visualisation of High-Dimensional Data.
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| 520 |
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|a 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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| 650 |
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|a Data mining.
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| 650 |
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|a Mathematics.
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| 650 |
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|a Visualization.
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| 650 |
1 |
4 |
|a Data Mining and Knowledge Discovery.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I18030
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| 650 |
2 |
4 |
|a Visualization.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M14034
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| 700 |
1 |
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|a Abonyi, János.
|e author.
|0 (orcid)0000-0001-8593-1493
|1 https://orcid.org/0000-0001-8593-1493
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer Nature eBook
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| 776 |
0 |
8 |
|i Printed edition:
|z 9781447151593
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| 776 |
0 |
8 |
|i Printed edition:
|z 9781447151579
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| 830 |
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|a SpringerBriefs in Computer Science,
|x 2191-5768
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-4471-5158-6
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| 912 |
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|a ZDB-2-SCS
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| 912 |
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|a ZDB-2-SXCS
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| 950 |
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|a Computer Science (SpringerNature-11645)
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| 950 |
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|a Computer Science (R0) (SpringerNature-43710)
|
