Diverse Cluster Ensemble in R
Compactness Measure
Consensus clustering
Combine algorithms
Evaluate, trim, and reweigh algorithms
Consensus matrix
Cluster-based Similarity Partitioning Algorithm (CSPA)
Diverse Clustering Ensemble
diceR: Diverse Cluster Ensemble in R
External validity indices
Graphical Displays
K-Nearest Neighbours imputation
Impute missing values
K-modes
Latent Class Analysis
Linkage Clustering Ensemble
Majority voting
Minimize Frobenius norm for between two matrices
Proportion of Ambiguous Clustering
Simulate and select null distributions on empirical gene-gene correlat...
Prepare data for consensus clustering
Relabel classes to a standard
Significant Testing of Clustering Results
Similarity Matrices
Performs cluster analysis using an ensemble clustering framework, Chiu & Talhouk (2018) <doi:10.1186/s12859-017-1996-y>. Results from a diverse set of algorithms are pooled together using methods such as majority voting, K-Modes, LinkCluE, and CSPA. There are options to compare cluster assignments across algorithms using internal and external indices, visualizations such as heatmaps, and significance testing for the existence of clusters.
Useful links