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 [aut, cre, cph], Quirin Stier [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:

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