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Titel:Projection-Based Clustering through Self-Organization and Swarm Intelligence
Autor:Thrun, Michael Christoph
Weitere Beteiligte: Ultsch, Alfred G. H. (Prof. Dr.)
Veröffentlicht:2018
URI:https://archiv.ub.uni-marburg.de/es/2018/0002
DOI: https://doi.org/10.17192/es2018.0002
URN: urn:nbn:de:hebis:04-es2018-00028
ISBN: 978-3-658-20540-9
DDC: Mathematik
Titel (trans.):Projection-Based Clustering über Selbstorganisation und Schwarmintelligenz
Publikationsdatum:2018-01-24
Lizenz:https://creativecommons.org/licenses/by/4.0

Dokument

Schlagwörter:
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

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.


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