Consensus Clustering using Multiple Algorithms and Parameters.
clusterCons - a package for consensus clustering in R
Calculate the Consensus Clustering Result from Re-Sampled Clustering Experiments with the Option of Using Multiple Algorithms and Parameters
Description
clusterCons contains functions for the calculation of robustness measures for clusters and cluster membership based on consensus matrices generated from bootstrapped clustering experiments in which a random proportion of rows of the data set are used in each individual clustering. This allows the user to prioritise clusters and the members of clusters based on their consistency in this regime.
The functions allow the user to select several algorithms to use in the re-sampling scheme and with any of the parameters that the algorithm would normally take.
Installation
The package can be installed from either .tar.gz (*nix) or .ZIP (windows) executables via the release on the right hand side of this web-page. These can be installed using the usual methods for R package installation either at the command line or within R.
clusterCons has recently been updated to be compatible with R version 4.1.2 and will soon re-appear on the CRAN repository after an absence of a few years. We will update this page as soon as it is available.