RelativeDifference

Calculates the difference between positive x and y values

Difference

relative

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>.

Michael Thrun

DatabionicSwarm

Swarm Intelligence for Self-Organized Clustering

Quirin Stier

RelativeDifference function

<dl><dt>X</dt>
<dd>either a value or numerical vector of [1:n]</dd><dt>Y</dt>
<dd>either a value or numerical vector of [1:n]</dd><dt>epsilon</dt>
<dd>Optional, If both x and y are approximatly zero the output is
also zero</dd><dt>na.rm</dt>
<dd>Optional, function does not work with non finite values. If these
cases should be automatically removed, set parameter TRUE</dd></dl>

Arguments

Author

Relative Difference — RelativeDifference

<dl>

<dt>X</dt>
<dd>either a value or numerical vector of [1:n]</dd>

<dt>Y</dt>
<dd>either a value or numerical vector of [1:n]</dd>

<dt>epsilon</dt>
<dd>Optional, If both x and y are approximatly zero the output is
also zero</dd>

<dt>na.rm</dt>
<dd>Optional, function does not work with non finite values. If these
cases should be automatically removed, set parameter TRUE</dd>

</dl>

RelativeDifference: Relative Difference

Description

Usage

Value

Arguments

Author

Details

References

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Examples