Multi-view agglomeration/splitting K-means clustering algorithm
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R Update simulation argument to reflect final iteration, initialize CIT… Oct 31, 2018
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DESCRIPTION
NAMESPACE
README.md

README.md

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 fuzzy 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").