maskmeans: Multi-view aggregation/splitting K-means clustering algorithm

The maskmeans package can be installed as follows:

library(devtools)
devtools::install_github("andreamrau/maskmeans")
library(maskmeans)

To also build the vignette, you can use the following (note that this will require the installation of some extra packages):

devtools::install_github("andreamrau/maskmeans", build_vignettes=TRUE)
library(maskmeans)

maskmeans incorporates algorithms for aggregating or splitting an existing hard or soft classification using multi-view data. The primary functions of this package are as follows:

• maskmeans, which itself calls one of the two following functions:
  • mv_aggregation
  • mv_splitting: Note that this algorithm allows either fixed multi-view weights across clusters (perCluster_mv_weights = FALSE) or per-cluster multi-view weights (perCluster_mv_weights = TRUE).
• maskmeans_cutree: cut an aggregation tree for a specified number of clusters
• mv_simulate to simulate data types "D1", ... "D6"

There are also two main plotting functions:

• mv_plot, to provide a plotting overview of multi-view data. Univariate views are plotted as density plots, bivariate views as scatterplots, and multivariate views as scatterplots of the first two principal components. A vector of cluster labels can be added to color the points according to a unique partition (e.g., the labels of the first view).
• maskmeans_plot, to plot results of the maskmeans function. Plot types provided through this function include type = c("dendrogram", "heights", "weights_line", "weights", "criterion", "tree")

See the package vignette for a full example and description. If the package was installed with the built vignette above, it may be accessed after loading the package via vignette("maskmeans").

If you use maskmeans in your research, please cite our work:

• Godichon-Baggioni, A., Maugis-Rabusseau, C. and Rau, A. (2020) Multi-view cluster aggregation and splitting, with an application to multi-omic breast cancer data. Annals of Applied Statistics, 14:2, 752-767. DOI: 10.1214/19-AOAS1317