About this book
This book is published open access under a CC BY 4.0 license.
It 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.
- Approaches to Unsupervised Machine Learning
- Methods of Visualization of High-Dimensional Data
- Quality Assessments of Visualizations
- Behavior-Based Systems in Data Science
- Databionic Swarm (DBS)
Lecturers, students as well as non-professional users of data science, statistics, computer science, business mathematics, medicine, biology
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.
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
- DOI https://doi.org/10.1007/978-3-658-20540-9
- Copyright Information The Editor(s) (if applicable) and The Author(s) 2018
- License CC BY
- Publisher Name Springer Vieweg, Wiesbaden
- eBook Packages Computer Science Computer Science (R0)
- Print ISBN 978-3-658-20539-3
- Online ISBN 978-3-658-20540-9
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