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diceR

Diverse Cluster Ensemble in R

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.

Functions (24)

CSPA

Cluster-based Similarity Partitioning Algorithm (CSPA)

LCA

Latent Class Analysis

LCE

Linkage Clustering Ensemble

PAC

Proportion of Ambiguous Clustering

compactness

Compactness Measure

consensus_cluster

Consensus clustering

consensus_combine

Combine algorithms

consensus_evaluate

Evaluate, trim, and reweigh algorithms

consensus_matrix

Consensus matrix

dice

Diverse Clustering Ensemble

diceR-package

diceR: Diverse Cluster Ensemble in R

external_validity

External validity indices

graphs

Graphical Displays

hgsc

Gene expression data for High Grade Serous Carcinoma from TCGA

impute_knn

K-Nearest Neighbours imputation

impute_missing

Impute missing values

k_modes

K-modes

majority_voting

Majority voting

min_fnorm

Minimize Frobenius norm for between two matrices

pcn

Simulate and select null distributions on empirical gene-gene correlations

prepare_data

Prepare data for consensus clustering

relabel_class

Relabel classes to a standard

sigclust

Significant Testing of Clustering Results

similarity

Similarity Matrices

diceR

Diverse Cluster Ensemble in R

v1.0.3
MIT + file LICENSE
Authors
Derek Chiu [aut, cre], Aline Talhouk [aut], Johnson Liu [ctb, com]
Initial release

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