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Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data
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.
Contents
Approaches to Unsupervised Machine LearningMethods of Visualization of High-Dimensional DataQuality Assessments of VisualizationsBehavior-Based Systems in Data ScienceDatabionic Swarm (DBS)
Target Groups
Lecturers, students as well as non-professional users of data science, statistics, computer science, business mathematics, medicine, biology
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.
This book is included in DOAB.
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Rights Information
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- 22 - pdf (None) at Google Books.
- 259 - pdf (CC BY) at Unglue.it.
Keywords
- 3D printing
- Advanced Analytics
- Analysis of Structured Data
- Analysis of stuctured data
- Cluster analysis
- Data science
- Dimensionality Reduction
- emergence
- Game theory
- High-dimensional data
- Knowledge Discovery
- Multivariate data
- self-organization
- Swarm intelligence
- Unsupervised machine learning
- Visualization
Links
web: https://link.springer.com/book/10.1007/978-3-658-20540-9Editions