scheme_sub_hc

Subsampling-based Hierarchical 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_hc 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>hc.method</dt>
<dd>hierarchical clustering method (default: "ward.D")</dd>
<dt>dist_method</dt>
<dd>distance method (default: "euclidean")</dd></dl>

Arguments

Subsampling-based Hierarchical Clustering — scheme_sub_hc

<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>hc.method</dt>
<dd>hierarchical clustering method (default: "ward.D")</dd>


<dt>dist_method</dt>
<dd>distance method (default: "euclidean")</dd>

</dl>

scheme_sub_hc: Subsampling-based Hierarchical Clustering

Description

Usage

Value

Arguments