DatabionicSwarm

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.

Help pages

32includes a package overview page

Examples that run

92%11 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)

Exported functions with examples

62%

Documented parameters

100%

Return-value docs

100%

Help pages citing references

84%

Installing DatabionicSwarm adds 24 packages (12 with compiled code, 18 MB of source tarballs). Heaviest: ggplot2 6.0 MB, Rcpp 1.8 MB, isoband 1.5 MB.

Pick two releases to compare their code metrics.

First published

2017-08-20

Total releases

14 / 9 yrs

License

GPL-3 OSI

Minimum R

≥ 3.0

Bundled data

18 KB / 3 files

Download size

1.0 MB

Cite this package

Run in R for the authors' entry:

The package provides a citation file, so citation() returns the authors' own entry.

It points to 10.1007/978-3-658-20540-9.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.