Projection-Based Clustering through Self-Organization and Swarm Intelligence

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-d...

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Bibliographic Details
Main Author: Thrun, Michael Christoph
Contributors: Ultsch, Alfred G. H. (Prof. Dr.) (Thesis advisor)
Format: Dissertation
Language:English
Published: Philipps-Universität Marburg 2018
Mathematik und Informatik
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Online Access:PDF Full Text
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Summary: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.
Physical Description:210 Pages
ISBN:978-3-658-20540-9
DOI:https://doi.org/10.17192/es2018.0002