KLMeasure

Computes rank-based smoothed precision/recall, with cost function based on Kullback-Leibler-divergence (see [Venna2010]).

Measure

precision

projection

recall

Kullback-Leibler-divergence

A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" <DOI:10.1007/s00357-020-09373-2>. Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, the t-SNE multicore version is based on C++ source code of Dmitry Ulyanov, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R.

Michael Thrun

ProjectionBasedClustering

Projection Based Clustering

Quirin Stier

Brinkmann Luca

Florian Lerch

Felix Pape

Tim Schreier

Luis Winckelmann

Kristian Nybo

Jarkko Venna

Ulyanov Dimitry 

van der Maaten Laurens

KLMeasure function

<dl> <dt>Data</dt>
<dd>numerical matrix of data: n cases in rows, d variables in columns</dd> <dt>pData</dt>
<dd>numerical matrix of projected data: n cases in rows, k variables in columns, where k is the projection output dimension</dd> <dt>NeighborhoodSize</dt>
<dd>Number of points in neighborhood to be considered. Default is 20</dd></dl>

Arguments

Author

Rank-based smoothed precision/recall measure for projection. — KLMeasure

<dl>

 <dt>Data</dt>
<dd>numerical matrix of data: n cases in rows, d variables in columns</dd>

 <dt>pData</dt>
<dd>numerical matrix of projected data: n cases in rows, k variables in columns, where k is the projection output dimension</dd>

 <dt>NeighborhoodSize</dt>
<dd>Number of points in neighborhood to be considered. Default is 20</dd>

</dl>

Rank-based smoothed precision/recall measure for projection.

KLMeasure: Rank-based smoothed precision/recall measure for projection.

Description

Usage

Value

Arguments

Author

References

See Also

Examples