Graph-Based Clustering and Data Visualization Algorithms (Record no. 57579)
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fixed length control field | 02756nam a22005055i 4500 |
001 - CONTROL NUMBER | |
control field | 978-1-4471-5158-6 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20200421112224.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 130525s2013 xxk| s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9781447151586 |
-- | 978-1-4471-5158-6 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 006.312 |
100 1# - AUTHOR NAME | |
Author | Vathy-Fogarassy, �Agnes. |
245 10 - TITLE STATEMENT | |
Title | Graph-Based Clustering and Data Visualization Algorithms |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | XIII, 110 p. 62 illus. |
490 1# - SERIES STATEMENT | |
Series statement | SpringerBriefs in Computer Science, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | Vector Quantisation and Topology-Based Graph Representation -- Graph-Based Clustering Algorithms -- Graph-Based Visualisation of High-Dimensional Data. |
520 ## - SUMMARY, ETC. | |
Summary, etc | 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. |
700 1# - AUTHOR 2 | |
Author 2 | Abonyi, J�anos. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | http://dx.doi.org/10.1007/978-1-4471-5158-6 |
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Koha item type | eBooks |
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-- | London : |
-- | Springer London : |
-- | Imprint: Springer, |
-- | 2013. |
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-- | computer |
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-- | online resource |
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-- | text file |
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer science. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Data mining. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Mathematics. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Visualization. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Science. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Data Mining and Knowledge Discovery. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Visualization. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 2191-5768 |
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