Consensus Clustering
Convert adjacency function to the affinity matrix
Covert data matrix to adjacency matrix
Generate clusters of data points from Gaussian distribution with given...
Generate clusters of data points from Gaussian distribution with rando...
Similarity between different clusters
Logit function
Consensus mechanism based on majority voting
Count the number of clusters based on stability score.
Relabeling clusters based on cluster similarities
Calculate the Co-cluster matrix for a given set of clustering results.
Build connectivity matrix
Calculate consensus matrix for data perturbation consensus clustering
Calculate consensus matrix for multi-data consensus clustering
Calculate consensus matrix for data perturbation consensus clustering
Generate clusters of data points from Gaussian-mixture-model distribut...
Generation mechanism for data perturbation consensus clustering
Generate a set of data points from Gaussian distribution
Multiple method generation
Multiview generation
Hierarchical clustering from adjacency matrix
Build indicator matrix
Multiple cluster generation
Multiple K-means generation
Multiple PAM (K-medoids) generation
Multiview cluster generation
Generate multiview clusters from Gaussian distributions with randomly ...
Multiview K-means generation
Multiview PAM (K-medoids) generation
PAM (k-medoids) clustering from adjacency matrix
Spectral clustering from adjacency matrix
Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) <doi:10.3389/fmicb.2023.1170391>.