Diverse Cluster Ensemble in R

Performs cluster analysis using an ensemble clustering framework. 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.


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Reference manual

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install.packages("diceR")

0.1.0 by Derek Chiu, 3 months ago


https://github.com/AlineTalhouk/diceR


Report a bug at https://github.com/AlineTalhouk/diceR/issues


Browse source code at https://github.com/cran/diceR


Authors: Derek Chiu [aut, cre], Aline Talhouk [aut], Johnson Liu [ctb, com]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports abind, assertthat, dplyr, magrittr, tidyr, purrr, ggplot2, gplots, grDevices, Hmisc, flux, NMF, apcluster, kernlab, mclust, infotheo, blockcluster, caret, class, clue, cluster, clusterCrit, clValid, klaR, RColorBrewer, quantable, RankAggreg, sigclust, methods, Rcpp

Suggests testthat, knitr, rmarkdown, covr

Linking to Rcpp


See at CRAN