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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>.

Version: 2.0.0
Depends: R (≥ 3.0)
Imports: Rcpp (≥ 1.0.8), RcppParallel (≥ 5.1.4), deldir, GeneralizedUmatrix, ABCanalysis, ggplot2
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: DataVisualizations, knitr (≥ 1.12), rmarkdown (≥ 0.9), plotrix, geometry, sp, spdep, parallel, rgl, png, ProjectionBasedClustering, parallelDist, pracma, dendextend
Published: 2024-06-20
DOI: 10.32614/CRAN.package.DatabionicSwarm
Author: Michael Thrun ORCID iD [aut, cre, cph], Quirin Stier ORCID iD [aut, rev]
Maintainer: Michael Thrun <m.thrun at gmx.net>
BugReports: https://github.com/Mthrun/DatabionicSwarm/issues
License: GPL-3
URL: https://www.deepbionics.org/
NeedsCompilation: yes
SystemRequirements: GNU make, pandoc (>=1.12.3, needed for vignettes)
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_2.0.0.tar.gz
Windows binaries: r-devel: DatabionicSwarm_2.0.0.zip, r-release: DatabionicSwarm_2.0.0.zip, r-oldrel: DatabionicSwarm_2.0.0.zip
macOS binaries: r-release (arm64): DatabionicSwarm_2.0.0.tgz, r-oldrel (arm64): DatabionicSwarm_2.0.0.tgz, r-release (x86_64): DatabionicSwarm_2.0.0.tgz, r-oldrel (x86_64): DatabionicSwarm_2.0.0.tgz
Old sources: DatabionicSwarm archive

Reverse dependencies:

Reverse imports: DRquality
Reverse suggests: FCPS, ProjectionBasedClustering

Linking:

Please use the canonical form https://CRAN.R-project.org/package=DatabionicSwarm to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.