Projection-Based Clustering Through Self-Organization and Swarm Intelligence: Combining Cluster Analysis With the Visualization of High-Dimensional Da - Hardcover

Projection-Based Clustering Through Self-Organization and Swarm Intelligence: Combining Cluster Analysis With the Visualization of High-Dimensional Da - Hardcover

$90.24
$90.24
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Projection-Based Clustering Through Self-Organization and Swarm Intelligence: Combining Cluster Analysis With the Visualization of High-Dimensional Da - Hardcover
  • Projection-Based Clustering Through Self-Organization and Swarm Intelligence: Combining Cluster Analysis With the Visualization of High-Dimensional Da - Hardcover
Projection-Based Clustering Through Self-Organization and Swarm Intelligence: Combining Cluster Analysis With the Visualization of High-Dimensional Da - Hardcover

Projection-Based Clustering Through Self-Organization and Swarm Intelligence: Combining Cluster Analysis With the Visualization of High-Dimensional Da - Hardcover

$90.24
$90.24

by Michael Christoph Thrun (Created by)

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.

This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Number of Pages: 212
Dimensions: 0.5 x 11 x 8.5 IN
Publication Date: October 12, 2020

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