setGridSize

Automatically sets the size of the grid, formula see [Thrun, 2018, p. 93-94].

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

setGridSize function

<dl><dt>InputDistances</dt>
<dd>[1:n,1:n] symmetric matrix of input distances</dd><dt>minp</dt>
<dd>default value: 0.01,see <code><a href="/link/quantile?package=DatabionicSwarm&version=2.0.0" data-mini-rdoc="DatabionicSwarm::quantile">quantile</a></code>, first value in the vector of <code>probs</code> estimates robust minimum of distances</dd><dt>maxp</dt>
<dd>default value: 0.99, see <code><a href="/link/quantile?package=DatabionicSwarm&version=2.0.0" data-mini-rdoc="DatabionicSwarm::quantile">quantile</a></code>, last value of the vector of <code>probs</code> estimates robust maximum of distances</dd><dt>alpha</dt>
<dd>Do not change! Intern parameter, Only if Java Version of Pswarm instead of C++ version is used.</dd><dt>Verbose</dt>
<dd>optional, integer stating degree of textual feedback. 0 = no output, 1 = basic
notifications, 2 = progress bar, 3 = details.</dd></dl>

Arguments

Author

Sets the grid size for the Pswarm algorithm — setGridSize

<dl>

<dt>InputDistances</dt>
<dd>[1:n,1:n] symmetric matrix of input distances</dd>

<dt>minp</dt>
<dd>default value: 0.01,see <code><a href='https://rdrr.io/r/stats/quantile.html'>quantile</a></code>, first value in the vector of <code>probs</code> estimates robust minimum of distances</dd>

<dt>maxp</dt>
<dd>default value: 0.99, see <code><a href='https://rdrr.io/r/stats/quantile.html'>quantile</a></code>, last value of the vector of <code>probs</code> estimates robust maximum of distances</dd>

<dt>alpha</dt>
<dd>Do not change! Intern parameter, Only if Java Version of Pswarm instead of C++ version is used.</dd>

<dt>Verbose</dt>
<dd>optional, integer stating degree of textual feedback. 0 = no output, 1 = basic
notifications, 2 = progress bar, 3 = details.</dd>

</dl>

setGridSize: Sets the grid size for the Pswarm algorithm

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples