https://doi.org/10.17192/es2018.0002
Projection-Based Clustering through Self-Organization and Swarm Intelligence
Thrun, Michael Christoph
Mathematics
Mathematik
ddc:510
Schwarmintelligenz ; Graphentheorie ; Cluster ; Emergenz ; Dimensionality Reduction ; Selbstorganisation ; Datenauswer ; Cluster Analysis ; Cluster-Analyse ; 3D pr ; Visualization ; Knowledge Discovery ; Unsupervised machine learning ; Schwarmintelligenz, Dimensionsreduktion ; Datenanalyse ; Spieltheorie ; Graphentheorie ; Datenanalyse , ; Swarm Intelligence ; Dimensionsreduktion ; Selbstorganisation ; Cluster Analyse ; Emergenz ; Spieltheorie ; Data science
Philipps-Universität Marburg
Ultsch, Alfred G. H. (Prof. Dr.)
2018
2018-01-24
DoctoralThesis
doc-type:doctoralThesis
Text
eng
https://creativecommons.org/licenses/by/4.0
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.