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.

Examines vector quantization methods, and discusses the advantages and disadvantages of minimal spanning tree-based clustering Presents a novel similarity measure to improve the classical Jarvis-Patrick clustering algorithm Reviews distance-, neighborhood- and topology-based dimensionality reduction methods, and introduces new graph-based visualization algorithms

Autorentext

Dr. János Abonyi is a Full Professor of Computer Science and Chemical Engineering at the Department of Process Engineering, University of Pannonia, Veszprém, Hungary. His other publications include the Springer titles Interpretability of Computational Intelligence-Based Regression Models, and (with Dr. Vathy-Fogarassy) Graph-Based Clustering and Data Visualization Algorithms. Dr. Ágnes Vathy-Fogarassy is an Associate Professor at the Department of Computer Science and Systems Technology at the University of Pannonia. Dániel Leitold is an Assistant Lecturer at the University of Pannonia.

Inhalt

Vector Quantisation and Topology-Based Graph Representation.- Graph-Based Clustering Algorithms.- Graph-Based Visualisation of High-Dimensional Data.