Package: DatabionicSwarm 1.3.0

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>.

Authors:Michael Thrun [aut, cre, cph], Quirin Stier [aut, rev]

DatabionicSwarm_1.3.0.tar.gz
DatabionicSwarm_1.3.0.zip(r-4.5)DatabionicSwarm_1.3.0.zip(r-4.4)DatabionicSwarm_1.3.0.zip(r-4.3)
DatabionicSwarm_1.3.0.tgz(r-4.4-arm64)DatabionicSwarm_1.3.0.tgz(r-4.4-x86_64)DatabionicSwarm_1.3.0.tgz(r-4.3-arm64)DatabionicSwarm_1.3.0.tgz(r-4.3-x86_64)
DatabionicSwarm_1.3.0.tar.gz(r-4.5-noble)DatabionicSwarm_1.3.0.tar.gz(r-4.4-noble)
DatabionicSwarm_1.3.0.tgz(r-4.4-emscripten)DatabionicSwarm_1.3.0.tgz(r-4.3-emscripten)
DatabionicSwarm.pdf |DatabionicSwarm.html
DatabionicSwarm/json (API)

# Install 'DatabionicSwarm' in R:
install.packages('DatabionicSwarm', repos = c('https://mthrun.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mthrun/databionicswarm/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • DefaultColorSequence - Default color sequence for plots
  • Hepta - Hepta is part of the Fundamental Clustering Problem Suit (FCPS) [Thrun/Ultsch, 2020].
  • Lsun3D - Lsun3D is part of the Fundamental Clustering Problem Suit (FCPS) [Thrun/Ultsch, 2020].

On CRAN:

11 exports 12 stars 2.29 score 33 dependencies 1 dependents 3 mentions 600 downloads

Last updated 9 months agofrom:442e97529e

Exports:DBSclusteringDelaunay4PointsDijkstraSSSPGeneratePswarmVisualizationProjectedPoints2GridPswarmRelativeDifferencesESOM4BMUssetGridSizeShortestGraphPathsCUniquePoints

Dependencies:clicolorspacedeldirfansifarverGeneralizedUmatrixggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppParallelrlangscalestibbleutf8vctrsviridisLitewithr

Short Intro to the Databionic Swarm (DBS)

Rendered fromDatabionicSwarm.Rmdusingknitr::rmarkdownon Jul 10 2024.

Last update: 2023-10-30
Started: 2018-06-15

Readme and manuals

Help Manual

Help pageTopics
Swarm Intelligence for Self-Organized ClusteringDatabionicSwarm-package DatabionicSwarm
Databonic swarm clustering (DBS)DBSclustering
Default color sequence for plotsDefaultColorSequence
Adjacency matrix of the delaunay graph for BestMatches of PointsDelaunay4Points
Delaunay Classification Error (DCE)DelaunayClassificationError
Intern functionDelta3DWeightsC
Internal function: Dijkstra SSSPDijkstraSSSP
Intern function, do not use yourselffindPossiblePositionsCsingle
Generates the Umatrix for Pswarm algorithmGeneratePswarmVisualization
Intern function: Transformation of Databot indizes to coordinatesgetCartesianCoordinates
depricated! see GeneralizedUmatrix() Generalisierte U-Matrix fuer ProjektionsverfahrengetUmatrix4Projection
Hepta is part of the Fundamental Clustering Problem Suit (FCPS) [Thrun/Ultsch, 2020].Hepta
Lsun3D is part of the Fundamental Clustering Problem Suit (FCPS) [Thrun/Ultsch, 2020].Lsun3D
Intern function for plotting during the Pswarm annealing processplotSwarm
Transforms ProjectedPoints to a gridProjectedPoints2Grid
A Swarm of Databots based on polar coordinates (Polar Swarm).Pswarm pswarm pswarmCpp
intern function, do not use yourselfPswarmCurrentRadiusC2botsPositive
Intern function for 'Pswarm'rDistanceToroidCsingle
Relative DifferenceRelativeDifference
Intern function: Simplified Emergent Self-Organizing MapsESOM4BMUs
setdiffMatrix shortens Matrix2Curt by those rows that are in both matrices.setdiffMatrix
Sets the grid size for the Pswarm algorithmsetGridSize
Intern function: Sets the polar gridsetPolarGrid
Intern function: Estimates the minimal radius for the Databot scentsetRmin
Shortest GraphPaths = geodesic distancesShortestGraphPathsC
Internal function for sESOMtrainstepC
Unique PointsUniquePoints