Graph-Based Clustering and Data Visualization Algorithms
Vathy-Fogarassy, Ágnes.
Graph-Based Clustering and Data Visualization Algorithms [electronic resource] / by Ágnes Vathy-Fogarassy, János Abonyi. - XIII, 110 p. 62 illus. online resource. - SpringerBriefs in Computer Science, 2191-5768 . - SpringerBriefs in Computer Science, .
Vector Quantisation and Topology-Based Graph Representation -- Graph-Based Clustering Algorithms -- Graph-Based Visualisation of High-Dimensional Data.
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.
9781447151586
10.1007/978-1-4471-5158-6 doi
Computer science.
Data mining.
Mathematics.
Visualization.
Computer Science.
Data Mining and Knowledge Discovery.
Visualization.
QA76.9.D343
006.312
Graph-Based Clustering and Data Visualization Algorithms [electronic resource] / by Ágnes Vathy-Fogarassy, János Abonyi. - XIII, 110 p. 62 illus. online resource. - SpringerBriefs in Computer Science, 2191-5768 . - SpringerBriefs in Computer Science, .
Vector Quantisation and Topology-Based Graph Representation -- Graph-Based Clustering Algorithms -- Graph-Based Visualisation of High-Dimensional Data.
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.
9781447151586
10.1007/978-1-4471-5158-6 doi
Computer science.
Data mining.
Mathematics.
Visualization.
Computer Science.
Data Mining and Knowledge Discovery.
Visualization.
QA76.9.D343
006.312