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  • Open Access
  • © 2018

Projection-Based Clustering through Self-Organization and Swarm Intelligence

Combining Cluster Analysis with the Visualization of High-Dimensional Data

Authors:

(view affiliations)
  • Enablement of Visualization with Clustering for Non-Professionals

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Softcover Book
USD 59.99
Price excludes VAT (USA)

Table of contents (14 chapters)

  1. Front Matter

    Pages I-XX
  2. Introduction

    • Michael Christoph Thrun
    Pages 1-3Open Access
  3. Fundamentals

    • Michael Christoph Thrun
    Pages 5-20Open Access
  4. Approaches to Cluster Analysis

    • Michael Christoph Thrun
    Pages 21-31Open Access
  5. Methods of Projection

    • Michael Christoph Thrun
    Pages 33-42Open Access
  6. Visualizing the Output Space

    • Michael Christoph Thrun
    Pages 43-53Open Access
  7. Quality Assessments of Visualizations

    • Michael Christoph Thrun
    Pages 55-75Open Access
  8. Behavior-based Systems in Data Science

    • Michael Christoph Thrun
    Pages 77-89Open Access
  9. Databionic Swarm (DBS)

    • Michael Christoph Thrun
    Pages 91-106Open Access
  10. Experimental Methodology

    • Michael Christoph Thrun
    Pages 107-116Open Access
  11. Results on Pre-classified Data Sets

    • Michael Christoph Thrun
    Pages 117-127Open Access
  12. DBS on Natural Data Sets

    • Michael Christoph Thrun
    Pages 129-136Open Access
  13. Knowledge Discovery with DBS

    • Michael Christoph Thrun
    Pages 137-148Open Access
  14. Discussion

    • Michael Christoph Thrun
    Pages 149-159Open Access
  15. Conclusion

    • Michael Christoph Thrun
    Pages 161-162Open Access
  16. Back Matter

    Pages 163-201

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. 

Contents
  • 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)
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.

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

Authors and Affiliations

  • Marburg, Germany

    Michael Christoph Thrun

About the authors

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

  • License: CC BY

  • Softcover ISBN: 978-3-658-20539-3

  • eBook ISBN: 978-3-658-20540-9

  • Edition Number: 1

  • Number of Pages: XX, 201

  • Number of Illustrations: 61 b/w illustrations, 29 illustrations in colour

  • Topics: Automated Pattern Recognition, Data Science

Buying options

Softcover Book
USD 59.99
Price excludes VAT (USA)