Package: DatabionicSwarm
Type: Package
License: GPL-3
Title: Swarm Intelligence for Self-Organized Clustering
Version: 1.3.0
Date: 2023-10-30
Authors@R: c(person("Michael", "Thrun", email= "m.thrun@gmx.net",role=c("aut","cre","cph"), comment = c(ORCID = "0000-0001-9542-5543")),person("Quirin", "Stier",role=c("aut","rev")))
Maintainer: Michael Thrun <m.thrun@gmx.net>
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, <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>.
Imports: Rcpp (>= 1.0.8), RcppParallel (>= 5.1.4), deldir,
        GeneralizedUmatrix
Suggests: DataVisualizations, knitr (>= 1.12), rmarkdown (>= 0.9),
        plotrix, geometry, sp, spdep, parallel, rgl, png,
        ProjectionBasedClustering, parallelDist, pracma, dendextend
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Depends: R (>= 3.0)
NeedsCompilation: yes
SystemRequirements: GNU make, pandoc (>=1.12.3, needed for vignettes)
LazyLoad: yes
LazyData: TRUE
URL: https://www.deepbionics.org/
Encoding: UTF-8
VignetteBuilder: knitr
BugReports: https://github.com/Mthrun/DatabionicSwarm/issues
Config/pak/sysreqs: make
Repository: https://mthrun.r-universe.dev
Date/Publication: 2023-10-30 12:31:37 UTC
RemoteUrl: https://github.com/mthrun/databionicswarm
RemoteRef: HEAD
RemoteSha: 442e97529e88b51ced34360722a23ede36a21e3e
Packaged: 2026-06-24 05:07:31 UTC; root
Author: Michael Thrun [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0001-9542-5543>),
  Quirin Stier [aut, rev]