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. 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 package is based on the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018)
Version 1.1.0 (2018-07-02 CRAN) o Delaunay Classification Error (DCE) added. DCE evaluates projection methods unbiased.
Version 1.0.7 (GitHub) o ClusteringAccuracy added. Given a prior Classification this function evaluates a clustering algorithm unbiased. o Now on GitHub.
Version 1.0.6 (Local) o Bugfix: error: Cube::operator(): index out of bounds
Version 1.0.5 (Local) o RelativeDifference added which calculates the difference of a positive x and y value in the range [-2,2].
Version 1.0.4 (Local) o Minor bugfixes o Vignette added
Version 1.0.3 (2018-05-06 CRAN) o Added distance methods of ParallelCpp package