DatabionicSwarm2.0.0 package

Swarm Intelligence for Self-Organized Clustering

Adjacency matrix of the delaunay graph for BestMatches of Points

intern function, do not use yourself

Internal function: Dijkstra SSSP

Intern function, do not use yourself

Generates the Umatrix for Pswarm algorithm

tools:::Rd_package_title("DatabionicSwarm")

Databonic swarm clustering (DBS)

Intern function: Transformation of Databot indizes to coordinates

depricated! see GeneralizedUmatrix() Generalisierte U-Matrix fuer Proj...

Intern function for plotting during the Pswarm annealing process

Transforms ProjectedPoints to a grid

A Swarm of Databots based on polar coordinates (Polar Swarm).

Intern function, do not use yourself

Intern function, do not use yourself

Intern function, do not use yourself

intern function, do not use yourself

Intern function for `Pswarm`

Relative Difference

Transforms the Robust Normalization back

RobustNormalization

Intern function: Simplified Emergent Self-Organizing Map

setdiffMatrix shortens Matrix2Curt by those rows that are in both matr...

Sets the grid size for the Pswarm algorithm

Intern function: Sets the polar grid

Intern function: Estimates the minimal radius for the Databot scent

Shortest GraphPaths = geodesic distances

internal function for s-esom

internal function for s-esom

Unique Points

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>.

Maintainer: Michael Thrun License: GPL-3 Last published: 2024-06-20