Overview
- Enablement of Visualization with Clustering for Non-Professionals
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Keywords
- Open Access
- Cluster Analysis
- Dimensionality Reduction
- Swarm Intelligence
- Visualization
- Unsupervised machine learning
- Data science
- Knowledge Discovery
- 3D printing
- Self-Organization
- Emergence
- Game theory
- Advanced Analytics
- High-dimensional data
- Multivariate data
- Analysis of stuctured data
- data structures
About this book
This open access book covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining.
Authors and Affiliations
About the author
Michael C. Thrun, Dipl.-Phys., successfully defended his Ph.D. in 2017 at the Philipps University of Marburg. Thrun’s advisor was the Chair of Neuroinformatics, Prof. Dr. rer. nat. Alfred G. H. Ultsch.
Bibliographic Information
Book Title: Projection-Based Clustering through Self-Organization and Swarm Intelligence
Book Subtitle: Combining Cluster Analysis with the Visualization of High-Dimensional Data
Authors: Michael Christoph Thrun
DOI: https://doi.org/10.1007/978-3-658-20540-9
Publisher: Springer Vieweg Wiesbaden
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2018
Softcover ISBN: 978-3-658-20539-3Published: 22 January 2018
eBook ISBN: 978-3-658-20540-9Published: 09 January 2018
Edition Number: 1
Number of Pages: XX, 201
Number of Illustrations: 61 b/w illustrations, 29 illustrations in colour
Topics: Pattern Recognition, Data Structures