getCartesianCoordinates

complex vector Two Indizes per Databot describing its positions in an two dimensional grid

DataBotsPos[1:N]

Radii Matrix of all possible Positions of DataBots in Grid, see also documentation of <code><a rd-options="" href="/link/setPolarGrid?package=DatabionicSwarm&version=1.1.0" data-mini-rdoc="DatabionicSwarm::setPolarGrid">setPolarGrid</a></code>

GridRadius[Lines,Columns]

Angle Matrix of all possible Positions of DataBots in Grid, see also documentation of <code><a rd-options="" href="/link/setPolarGrid?package=DatabionicSwarm&version=1.1.0" data-mini-rdoc="DatabionicSwarm::setPolarGrid">setPolarGrid</a></code>

GridAngle[Lines,Columns]

Size of planar toroid two dimensional grid

Lines,Columns

Optional, FALSE=If DataPos on hexadiagonal grid, round to 2 decimals after value, Default=TRUE

QuadOrHexa

Transforms Databot indizes to exact cartesian coordinates on an toroid two dimensional grid.

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) <DOI:10.1007/978-3-658-20540-9>. A comparison to 26 common clustering algorithms on 15 datasets is presented on the website.

Michael Thrun

DatabionicSwarm

Swarm Intelligence for Self-Organized Clustering

getCartesianCoordinates function

Radii Matrix of all possible Positions of DataBots in Grid, see also documentation of <code><a rd-options='' href='setPolarGrid'>setPolarGrid</a></code>

Angle Matrix of all possible Positions of DataBots in Grid, see also documentation of <code><a rd-options='' href='setPolarGrid'>setPolarGrid</a></code>

getCartesianCoordinates: Intern function: Transformation of Databot indizes to coordinates

Description

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Value

Details

References