SparseKMeansClustering

Runs sparse K means clustering using implementation from
<code>KMeansSparseCluster</code>. This function is not using
stability.

internal

In stability selection (N Meinshausen, P Bühlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>) and consensus clustering (S Monti et al (2003) <doi:10.1023/A:1023949509487>), resampling techniques are used to enhance the reliability of the results. In this package, hyper-parameters are calibrated by maximising model stability, which is measured under the null hypothesis that all selection (or co-membership) probabilities are identical (B Bodinier et al (2023a) <doi:10.1093/jrsssc/qlad058> and B Bodinier et al (2023b) <doi:10.1093/bioinformatics/btad635>). Functions are readily implemented for the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO in stability selection, and hierarchical clustering, partitioning around medoids, K means or Gaussian mixture models in consensus clustering.

Barbara Bodinier

sharp

Stability-enHanced Approaches using Resampling Procedures

SparseKMeansClustering function

<dl><dt>xdata</dt>
<dd>data matrix with observations as rows and variables as columns.</dd>
<dt>nc</dt>
<dd>matrix of parameters controlling the number of clusters in the
underlying algorithm specified in <code>implementation</code>. If <code>nc</code> is
not provided, it is set to <code>seq(1, tau*nrow(xdata))</code>.</dd>
<dt>Lambda</dt>
<dd>vector of penalty parameters (see argument <code>wbounds</code> in
<code>KMeansSparseCluster</code>).</dd>
<dt>...</dt>
<dd>additional parameters passed to
<code>KMeansSparseCluster</code>.</dd></dl>

Arguments

Sparse K means clustering — SparseKMeansClustering

<dl>

<dt>xdata</dt>
<dd>data matrix with observations as rows and variables as columns.</dd>


<dt>nc</dt>
<dd>matrix of parameters controlling the number of clusters in the
underlying algorithm specified in <code>implementation</code>. If <code>nc</code> is
not provided, it is set to <code>seq(1, tau*nrow(xdata))</code>.</dd>


<dt>Lambda</dt>
<dd>vector of penalty parameters (see argument <code>wbounds</code> in
<code>KMeansSparseCluster</code>).</dd>


<dt>...</dt>
<dd>additional parameters passed to
<code>KMeansSparseCluster</code>.</dd>

</dl>

SparseKMeansClustering: Sparse K means clustering

Description

Usage

Value

Arguments

References