Graph-Based Clustering and Data Visualization Algorithms

Nonfiction, Science & Nature, Mathematics, Graphic Methods, Computers, Database Management, General Computing
Cover of the book Graph-Based Clustering and Data Visualization Algorithms by Ágnes Vathy-Fogarassy, János Abonyi, Springer London
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Ágnes Vathy-Fogarassy, János Abonyi ISBN: 9781447151586
Publisher: Springer London Publication: May 24, 2013
Imprint: Springer Language: English
Author: Ágnes Vathy-Fogarassy, János Abonyi
ISBN: 9781447151586
Publisher: Springer London
Publication: May 24, 2013
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer London

Cover of the book Atlas of Operative Maxillofacial Trauma Surgery by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Practical Carotid Artery Stenting by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Heart Failure in Clinical Practice by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Handbook of Iris Recognition by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Visualizing Argumentation by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Social Media on the Road by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Cancer Chemotherapy in Clinical Practice by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Leadership in Healthcare by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Neuromuscular Diseases by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Mathematics for Computer Graphics by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book 802.11 Wireless Networks by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Vulnerable Systems by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Dynamic Thermal Analysis of Machines in Running State by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book Economic Modeling Using Artificial Intelligence Methods by Ágnes Vathy-Fogarassy, János Abonyi
Cover of the book An Atlas of Rectal Endosonography by Ágnes Vathy-Fogarassy, János Abonyi
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy