Projection-Based Clustering through Self-Organization and Swarm Intelligence

Combining Cluster Analysis with the Visualization of High-Dimensional Data
1. Auflage 2018
ISBN: 978-3-658-20539-3
Verlag: Springer

Buch, Englisch, 201 Seiten, Format (B × H): 168 mm x 240 mm, Gewicht: 384 g

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. 

Zielgruppe

Research

Autoren/Hrsg.

Thrun, Michael Christoph

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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).

Über Autor(innen)

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.