Graph-based clustering and data visualization algorithms:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London [u.a.]
Springer
2013
|
| Schriftenreihe: | Springer briefs in computer science
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 110 S. graph. Darst. |
| ISBN: | 9781447151579 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804150527451201536 |
|---|---|
| adam_text | Contents
1
Vector
Quantisation and Topology Based Graph
Representation
....................................... 1
1.1
Building Graph from Data
...........................
I
1.2
Vector Quantisation Algorithms
....................... 2
1.2.1
fc-Means Clustenng
........................... 3
1.2.2
Neural Gas Vector Quantisation
.................. 5
1.2.3
Growing Neural Gas Vector Quantisation
........... 6
1.2.4
Topology Representing Network
.................. 9
1.2.5
Dynamic Topology Representing Network
........... 11
1.2.6
Weighted Incremental Neural Network
............. 13
References
. . .
ι
...................................... 16
2
Graph-Based Clustering Algorithms
....................... 17
2.1
Neigborhood-Graph-Based Clustering
................... 17
2.2
Minimal Spanning Tree Based Clustenng
................. 18
2.2.1
Hybrid MST: Gath-Geva Clustenng Algonthm
........ 21
2.2.2
Analysis and Application Examples
............... 24
2.3
Jarvis-Patnck Clustenng
............................. 30
2.3.1
Fuzzy Similarity Measures
...................... 31
2.3.2
Application of Fuzzy Similanty Measures
........... 33
2.4
Summary of Graph-Based Clustenng Algorithms
........... 39
References
.......................................... 40
3
Graph-Based Visualisation of High Dimensional Data
.......... 43
3.1
Problem of Dimensionality Reduction
................... 43
3.2
Measures of the Mapping Quality
...................... 46
3.3
Standard Dimensionality Reduction Methods
.............. 49
3.3.1
Principal Component Analysis
................... 49
3.3.2
Sammon Mapping
............................ 51
3.3.3
Multidimensional Scaling
....................... 52
x
Contents
3.4
Neighbourhood-Based Dimensionality Reduction
........... 55
3.4.1
Locality Preserving Projections
................... 55
3.4.2
Self-Organizing Map
.......................... 57
3.4.3
Incremental Grid Growing
...................... 59
3.5
Topology Representation
............................ 61
3.5.1 Isomap.................................... 62
3.5.2 Isotop.................................... 64
3.5.3
Curvilinear Distance Analysis
.................... 65
3.5.4
Online Data Visualisation Using Neural Gas Network.
. . 67
3.5.5
Geodesic Nonlinear Projection Neural Gas
........... 68
3.5.6
Topology Representing Network Map
.............. 70
3.6
Analysis and Application Examples
..................... 74
3.6.1
Comparative Analysis of Different Combinations
...... 74
3.6.2
Swiss Roll Data Set
.......................... 76
3.6.3
Wine Data Set
.............................. 81
3.6.4
Wisconsin Breast Cancer Data Set
................ 85
3.7
Summary of Visualisation Algorithms
................... 87
References
.......................................... 88
Appendix
............................................. 93
Index
................................................ 109
|
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| ctrlnum | (OCoLC)856817028 (DE-599)BVBBV041133054 |
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| illustrated | Illustrated |
| indexdate | 2024-07-10T00:40:20Z |
| institution | BVB |
| isbn | 9781447151579 |
| language | English |
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| physical | XIII, 110 S. graph. Darst. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
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| series2 | Springer briefs in computer science |
| spelling | Vathy-Fogarassy, Ágnes Verfasser aut Graph-based clustering and data visualization algorithms Ágnes Vathy-Fogarassy; János Abonyi London [u.a.] Springer 2013 XIII, 110 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Springer briefs in computer science Custer (DE-588)10320755-7 gnd rswk-swf Visualisierung (DE-588)4188417-6 gnd rswk-swf Visualisierung (DE-588)4188417-6 s Custer (DE-588)10320755-7 b DE-604 Abonyi, János 1974- Sonstige (DE-588)133119246 oth Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026108827&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Vathy-Fogarassy, Ágnes Graph-based clustering and data visualization algorithms Custer (DE-588)10320755-7 gnd Visualisierung (DE-588)4188417-6 gnd |
| subject_GND | (DE-588)10320755-7 (DE-588)4188417-6 |
| title | Graph-based clustering and data visualization algorithms |
| title_auth | Graph-based clustering and data visualization algorithms |
| title_exact_search | Graph-based clustering and data visualization algorithms |
| title_full | Graph-based clustering and data visualization algorithms Ágnes Vathy-Fogarassy; János Abonyi |
| title_fullStr | Graph-based clustering and data visualization algorithms Ágnes Vathy-Fogarassy; János Abonyi |
| title_full_unstemmed | Graph-based clustering and data visualization algorithms Ágnes Vathy-Fogarassy; János Abonyi |
| title_short | Graph-based clustering and data visualization algorithms |
| title_sort | graph based clustering and data visualization algorithms |
| topic | Custer (DE-588)10320755-7 gnd Visualisierung (DE-588)4188417-6 gnd |
| topic_facet | Custer Visualisierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026108827&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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