DatabionicSwarm: Swarm Intelligence for Self-Organized Clustering

Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called Databionic swarm (DBS) is introduced which was published in Thrun, M.C., Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020), Artificial Intelligence, <doi:10.1016/j.artint.2020.103237>. DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The comparison to common projection methods can be found in the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <doi:10.1007/978-3-658-20540-9>. A comparison to 26 common clustering algorithms on 15 datasets is presented on the website.

Version: 1.1.5
Depends: R (≥ 3.0)
Imports: Rcpp, deldir, GeneralizedUmatrix
LinkingTo: Rcpp, RcppArmadillo
Suggests: DataVisualizations, knitr (≥ 1.12), rmarkdown (≥ 0.9), plotrix, geometry, sp, spdep, AdaptGauss, ABCanalysis, parallel, rgl, png, ProjectionBasedClustering, parallelDist, pracma, dendextend
Published: 2021-01-12
Author: Michael Thrun ORCID iD [aut, cre, cph]
Maintainer: Michael Thrun <m.thrun at gmx.net>
BugReports: https://github.com/Mthrun/DatabionicSwarm/issues
License: GPL-3
URL: http://www.deepbionics.org
NeedsCompilation: yes
SystemRequirements: C++11
Citation: DatabionicSwarm citation info
Materials: NEWS
In views: Cluster
CRAN checks: DatabionicSwarm results

Documentation:

Reference manual: DatabionicSwarm.pdf
Vignettes: Short Intro to the Databionic Swarm (DBS)

Downloads:

Package source: DatabionicSwarm_1.1.5.tar.gz
Windows binaries: r-devel: DatabionicSwarm_1.1.5.zip, r-release: DatabionicSwarm_1.1.5.zip, r-oldrel: DatabionicSwarm_1.1.5.zip
macOS binaries: r-release (arm64): DatabionicSwarm_1.1.5.tgz, r-oldrel (arm64): DatabionicSwarm_1.1.5.tgz, r-release (x86_64): DatabionicSwarm_1.1.5.tgz, r-oldrel (x86_64): DatabionicSwarm_1.1.5.tgz
Old sources: DatabionicSwarm archive

Reverse dependencies:

Reverse suggests: DataVisualizations, FCPS, ProjectionBasedClustering

Linking:

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