knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig_path = "README-" )
CrossClustering is a partial clustering algorithm that combines the Ward's minimum variance and Complete Linkage algorithms, providing automatic estimation of a suitable number of clusters and identification of outlier elements.
This is a basic example which shows you how to the main function, i.e.
cc_crossclustering()
works:
## basic example code library(CrossClustering) #### method = "complete" data(toy) ### toy is transposed as we want to cluster samples (columns of the original ### matrix) d <- dist(t(toy), method = "euclidean") ### Run CrossClustering toyres <- cc_crossclustering( d, k_w_min = 2, k_w_max = 5, k2_max = 6, out = TRUE ) toyres
Another useful function worth to mention is ari
:
clusters <- iris[-5] |> dist() |> hclust(method = 'ward.D') |> cutree(k = 3) ground_truth <- iris[[5]] |> as.numeric() table(ground_truth, clusters) |> ari()
CrossClustering package is on CRAN, use the standard method to install it.
install_packages('CrossClustering')
To install the develop branch of CrossClastering package, use:
# install.packages(devtools) devtools::install_github('CorradoLanera/CrossClustering', ref = 'develop')
If you encounter a bug, please file a reprex (minimal reproducible example) to https://github.com/CorradoLanera/CrossClustering/issues
Tellaroli P, Bazzi M., Donato M., Brazzale A. R., Draghici S. (2016). Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters. PLoS ONE 11(3): e0152333. https://doi.org/10.1371/journal.pone.0152333
Tellaroli P, Bazzi M., Donato M., Brazzale A. R., Draghici S. (2017). E1829: Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters. CMStatistics 2017, London 16-18 December, Book of Abstracts (ISBN 978-9963-2227-4-2)
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