TY  - BOOK
AU  - Vathy-Fogarassy,Ágnes
AU  - Abonyi,János
ED  - SpringerLink (Online service)
TI  - Graph-Based Clustering and Data Visualization Algorithms
T2  - SpringerBriefs in Computer Science,
SN  - 9781447151586
AV  - QA76.9.D343 
U1  - 006.312 23
PY  - 2013///
CY  - London
PB  - Springer London, Imprint: Springer
KW  - Computer science
KW  - Data mining
KW  - Mathematics
KW  - Visualization
KW  - Computer Science
KW  - Data Mining and Knowledge Discovery
N1  - Vector Quantisation and Topology-Based Graph Representation -- Graph-Based Clustering Algorithms -- Graph-Based Visualisation of High-Dimensional Data
N2  - 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
UR  - http://dx.doi.org/10.1007/978-1-4471-5158-6
ER  -