Bootstrapping Estimates of Clustering Stability
Calculate agreement between two clustering results with known number o...
Calculate agreement between two clustering results
Multi-Method Ensemble Clustering Analysis for Multiple-Objective Clust...
Calculate Comparison Statistics
Calculate Stability Measures for a Clustering Method
Compare MOC Results
Create Graph and Find Communities Using Different Methods
Create Incidence Matrix for Graph Construction
Define Stability Combination Methods
Multi-Method Ensemble Clustering with Multiple Stability Combinations
Multi-Method Ensemble Clustering with Graph-based Consensus
Estimate the stability of a clustering based on non-parametric bootstr...
Estimate number of clusters
Estimate number of clusters
Load Multiple-Objective Clustering (MOC) Datasets
Calculate minimum agreement across clusters
Plot method for objests from threshold.select
Estimate of detect module stability
Estimate the stability of a clustering based on non-parametric bootstr...
Create a Grid Plot of MOC Results
Plot MOC Results
Generate reference distribution for binary data
Generate PCA-based reference distribution
Generate reference distribution for stability assessment
Subsampling-based Hierarchical Clustering
Subsampling-based K-means Clustering
Subsampling-based Spectral Clustering
Estimate clustering stability of k-means
Estimate of the overall Jaccard stability
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.