Graph Based Clustering And Data Visualization Algorithms

Download or read book Graph-Based Clustering and Data Visualization Algorithms written by Ágnes Vathy-Fogarassy and published by Springer Science & Business Media. This book was released on 2013-05-24 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.