Package: DatabionicSwarm
Type: Package
Title: Swarm Intelligence for Self-Organized Clustering
Version: 1.1.3
Date: 2020-02-02
Authors@R: person("Michael", "Thrun", email= "m.thrun@gmx.net",role=c("aut","cre","cph"))
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>. A comparison to 26 common clustering algorithms on 15 datasets is presented on the website.
License: GPL-3
Imports: Rcpp, deldir, GeneralizedUmatrix
Suggests: DataVisualizations, knitr (>= 1.12), rmarkdown (>= 0.9),
        plotrix, geometry, sp, spdep, AdaptGauss, ABCanalysis,
        parallel, matrixStats, rgl, png, ProjectionBasedClustering,
        parallelDist, pracma, dendextend
LinkingTo: Rcpp, RcppArmadillo
Depends: R (>= 3.0)
NeedsCompilation: yes
SystemRequirements: C++11
LazyLoad: yes
LazyData: TRUE
URL: http://www.deepbionics.org
Encoding: UTF-8
VignetteBuilder: knitr
BugReports: https://github.com/Mthrun/DatabionicSwarm/issues
Packaged: 2020-02-03 13:01:00 UTC; MT
Author: Michael Thrun [aut, cre, cph]
Repository: CRAN
Date/Publication: 2020-02-03 14:00:02 UTC
Built: R 3.5.3; x86_64-w64-mingw32; 2020-04-22 08:51:36 UTC; windows
Archs: i386, x64
