UniquePoints

return only the unique points in Datapoints

points

unique

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

UniquePoints function

<dl> <dt>Datapoints</dt>
<dd>[1:n,1:d] matrix of Datapoints points of dimension d, the points are in the rows</dd> <dt>Eps</dt>
<dd>Optional,scalar above zero that defines minimum non-identical euclidean distance between two points</dd></dl>

Arguments

Author

Unique Points — UniquePoints

<dl>

 <dt>Datapoints</dt>
<dd>[1:n,1:d] matrix of Datapoints points of dimension d, the points are in the rows</dd>

 <dt>Eps</dt>
<dd>Optional,scalar above zero that defines minimum non-identical euclidean distance between two points</dd>

</dl>

UniquePoints: Unique Points

Description

Usage

Value

Arguments

Author

Details

Examples