scheme_sub_km

Subsampling-based K-means Clustering

internal

Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016)<doi:10.1142/9789814749411_0007>. Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) <doi:10.1002/sam.11495>. Implemented out-of-bag stability estimation function and k-select Smin-based k-selection function as introduced by Liu et al (2022) <doi:10.1002/sam.11593>. Implemented ensemble clustering method based-on k-means clustering method, spectral clustering method and hierarchical clustering method.

Tianmou Liu

bootcluster

Bootstrapping Estimates of Clustering Stability

Han Yu

Mingmei Tian

scheme_sub_km function

<dl><dt>df</dt>
<dd>data.frame or matrix of the dataset</dd>
<dt>nk</dt>
<dd>number of clusters</dd>
<dt>B</dt>
<dd>number of bootstrap samples</dd>
<dt>cut_ratio</dt>
<dd>ratio for subsampling (default: 0.5)</dd>
<dt>r</dt>
<dd>number of k-means runs (default: 5)</dd></dl>

Arguments

Subsampling-based K-means Clustering — scheme_sub_km

<dl>

<dt>df</dt>
<dd>data.frame or matrix of the dataset</dd>


<dt>nk</dt>
<dd>number of clusters</dd>


<dt>B</dt>
<dd>number of bootstrap samples</dd>


<dt>cut_ratio</dt>
<dd>ratio for subsampling (default: 0.5)</dd>


<dt>r</dt>
<dd>number of k-means runs (default: 5)</dd>

</dl>

scheme_sub_km: Subsampling-based K-means Clustering

Description

Usage

Value

Arguments