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, . 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) . A comparison to 26 common clustering algorithms on 15 datasets is presented on the website.


Version 1.1.1 (2018-07-10 GitHub) o bugfix: RelativeDifference now stops if non finite values in either x and y expaining the error

Version 1.1.0 (2018-06-26 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

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.