ccphate

Consensus Clustering with PHATE Geometry

Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis.

Kisung You

T4cluster

Tools for Cluster Analysis

ccphate function

<dl><dt>data</dt>
<dd>an \((n\times p)\) matrix of row-stacked observations.</dd>
<dt>k</dt>
<dd>the number of clusters (default: 2).</dd>
<dt>...</dt>
<dd>extra parameters including<dl>
<dt>nnbd</dt>
<dd>size of nearest neighborhood (default: 5).</dd></dl></dd><dt>alpha</dt>
<dd>decay parameter for Gaussian kernel exponent (default: 2).</dd><dt>ratio</dt>
<dd>percentage of samples to be used for each resampling run with the minimum is set as 0.25 (default: 0.9).</dd><dt>niter</dt>
<dd>number of resampling runs where the minimum is set 50 (default: 100).</dd>
</dl>

Arguments

Consensus Clustering with PHATE Geometry — ccphate

<dl>

<dt>data</dt>
<dd>an \((n\times p)\) matrix of row-stacked observations.</dd>


<dt>k</dt>
<dd>the number of clusters (default: 2).</dd>


<dt>...</dt>
<dd>extra parameters including<dl>
<dt>nnbd</dt>
<dd>size of nearest neighborhood (default: 5).</dd>

<dt>alpha</dt>
<dd>decay parameter for Gaussian kernel exponent (default: 2).</dd>

<dt>ratio</dt>
<dd>percentage of samples to be used for each resampling run with the minimum is set as 0.25 (default: 0.9).</dd>

<dt>niter</dt>
<dd>number of resampling runs where the minimum is set 50 (default: 100).</dd>


</dl></dd>

</dl>

ccphate: Consensus Clustering with PHATE Geometry

Description

Usage

Arguments