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Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

Inside this Book

If you make use of this material, you may credit the authors as follows:

Michael Christoph Thrun, "Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data", Springer Nature, 2018, DOI: https://doi.org/10.1007/978-3-658-20540-9, License: https://creativecommons.org/licenses/by/4.0/

This 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.

Keywords

Visualization, Knowledge Discovery, Swarm Intelligence, Unsupervised Machine Learning, Data Science, Game Theory, 3d Printing, Dimensionality Reduction, Multivariate Data, Analysis Of Structured Data, Self-organization, Emergence, Advanced Analytics, High-dimensional Data, Cluster Analysis

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