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 |
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.
Das Dokument ist im Internet frei zugänglich - Hinweise zu den Nutzungsrechten |