CrossClustering

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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.

Example

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
 #> 
 #> CrossClustering with method complete.
 #> 
 #> Parameter used:
 #> - Interval for the number of cluster of Ward's algorithm: [2, 5].
 #> - Interval for the number of cluster of the complete algorithm: [2, 6].
 #> - Outliers are considered.
 #> 
 #> Number of clusters found: 3.
 #> Leading to an avarage silhouette width of: 0.8405.
 #> 
 #> A total of 6 elements clustered out of 7 elements considered.

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()
 #> Adjusted Rand Index (alpha = 0.05)
 #> 
 #> ARI = 0.76 (moderate recovery)
 #> Confidence interval = [0.74, 0.78]
 #> 
 #> p-values:
 #> * Qannari test = < 0.001
 #> * Permutation test = 0.001

Install

CRAN version

CrossClustering package is on CRAN, use the standard method to install it. install_packages('CrossClustering')

develop version

To install the develop branch of CrossClastering package, use:

 # install.packages(devtools)
devtools::install_github('CorradoLanera/CrossClustering', ref = 'develop')

Bug reports

If you encounter a bug, please file a reprex (minimal reproducible example) to https://github.com/CorradoLanera/CrossClustering/issues

References

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年4月2日)