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DatabionicSwarm
View on CRAN: Click
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Download and install DatabionicSwarm package within the R console
Install from CRAN:
install.packages("DatabionicSwarm")
Install from Github:
library("remotes")
install_github("cran/DatabionicSwarm") Install by package version:
library("remotes")
install_version("DatabionicSwarm", "2.0.0") Attach the package and use:
library("DatabionicSwarm")
Maintained by
Michael Thrun
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[Scholar Profile | Author Map]
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First Published: 2017-08-20
Latest Update: 2024-06-20
Description:
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, . 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) .
How to cite:
Michael Thrun (2017). DatabionicSwarm: Swarm Intelligence for Self-Organized Clustering. R package version 2.0.0, https://cran.r-project.org/web/packages/DatabionicSwarm. Accessed 30 Aug. 2026.
Previous versions and publish date:
0.9.7 (2017-08-20 13:13), 0.9.8 (2017-09-28 19:55), 1.0.0 (2018-01-31 19:00), 1.0.1 (2018-03-07 08:51), 1.0.3 (2018-05-06 18:45), 1.1.0 (2018-07-03 10:30), 1.1.1 (2019-01-27 15:20), 1.1.2 (2019-12-11 18:30), 1.1.3 (2020-02-03 15:00), 1.1.5 (2021-01-12 20:20), 1.1.6 (2022-11-29 09:50), 1.2.0 (2023-05-30 10:50), 1.2.1 (2023-10-13 13:30), (2026-07-09 08:02)
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Complete documentation for DatabionicSwarm
Functions, R codes and Examples using
the DatabionicSwarm R package
Some associated functions: ClusteringAccuracy . DBSclustering . DatabionicSwarm-package . DefaultColorSequence . Delaunay4Points . DelaunayClassificationError . Delta3DWeightsC . DijkstraSSSP . GeneratePswarmVisualization . Hepta . Lsun3D . ProjectedPoints2Grid . Pswarm . PswarmCurrentRadiusC2botsPositive . RelativeDifference . RobustNorm_BackTrafo . RobustNormalization . ShortestGraphPathsC . UniquePoints . findPossiblePositionsCsingle . getCartesianCoordinates . getUmatrix4Projection . rDistanceToroidCsingle . sESOM4BMUs . setGridSize . setPolarGrid . setRmin . setdiffMatrix . trainstepC .
Some associated R codes: ClusteringAccuracy.R . DBSclustering.R . Delaunay4Points.R . DelaunayClassificationError.R . GeneratePswarmVisualization.R . ProjectedPoints2Grid.R . Pswarm.R . RcppExports.R . RelativeDifference.R . RobustNorm_BackTrafo.R . RobustNormalization.R . ShortestGraphPathsC.R . UniquePoints.R . calcGabrielGraph2D.R . checkInputDistancesOrData.R . getCartesianCoordinates.R . getUmatrix4Projection.R . plotSwarm.R . sESOM4BMUs.R . setGridSize.R . setPolarGrid.R . setRmin.R . startupmsg.R . Full DatabionicSwarm package functions and examples
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