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bootcluster

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.

To install this package, please use

install.packages("bootcluster")

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Install

install.packages('bootcluster')

Monthly Downloads

281

Version

0.4.2

License

GPL-2

Maintainer

Tianmou Liu

Last Published

June 17th, 2025

Functions in bootcluster (0.4.2)

load_moc_datasets

Load Multiple-Objective Clustering (MOC) Datasets
ob.stability

Estimate the stability of a clustering based on non-parametric bootstrap out-of-bag scheme, with option for subsampling scheme
scheme_sub_hc

Subsampling-based Hierarchical Clustering
min_agreement

Calculate minimum agreement across clusters
plot_moc_grid

Create a Grid Plot of MOC Results
stability

Estimate clustering stability of k-means
scheme_sub_km

Subsampling-based K-means Clustering
scheme_sub_spectral

Subsampling-based Spectral Clustering
wine

Wine Data Set
threshold.select

Estimate of the overall Jaccard stability
k.select_ref

Estimate number of clusters
calculate_stability_measures

Calculate Stability Measures for a Clustering Method
analyze_moc_datasets

Multi-Method Ensemble Clustering Analysis for Multiple-Objective Clustering (MOC) Datasets
agreement

Calculate agreement between two clustering results
agreement_nk

Calculate agreement between two clustering results with known number of clusters
ensemble.cluster.multi

Multi-Method Ensemble Clustering with Graph-based Consensus
compare_moc_results

Compare MOC Results
network.stability.output

Plot method for objests from threshold.select
ensemble_cluster_multi_combinations

Multi-Method Ensemble Clustering with Multiple Stability Combinations
esmbl.stability

Estimate the stability of a clustering based on non-parametric bootstrap out-of-bag scheme, with option for subsampling scheme
k.select

Estimate number of clusters
network.stability

Estimate of detect module stability
define_combination_methods

Define Stability Combination Methods
calculate_comparison_stats

Calculate Comparison Statistics
ref_dist

Generate reference distribution for stability assessment
plot_moc_results

Plot MOC Results
create_graph_and_communities

Create Graph and Find Communities Using Different Methods
create_incidence_matrix

Create Incidence Matrix for Graph Construction
ref_dist_pca

Generate PCA-based reference distribution
ref_dist_bin

Generate reference distribution for binary data