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Graph-Based Clustering and Data Visualization Algorithms

SBCS: GRAPH-BASED CLUSTERING & DATA VSLZTN ALGORITHMS

Information Technology

Computing & Information Technology

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B2B GT

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B2B & B2C

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.

POST SECONDARY FOUR YEAR

COLFR

COLSO

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COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Other Frontmatter.
Title Page.
Copyright Page.
Preface.
Contents.
Acronyms.
Symbols.
1: Vector Quantisation and Topology Based Graph Representation.
2: Graph-Based Clustering Algorithms.
3: Graph-Based Visualisation of High Dimensional Data.
Appendix: Constructing a Minimum Spanning Tree.
Index.

Graph-Based Clustering and Data Visualization Algorithms.css-1p8rvzy{position:absolute;padding-left:8px;}

Graph-Based Clustering and Data Visualization Algorithms