mv_aggregation: Aggregation of hard or soft clusters based on multi-view data

In andreamrau/maskmeans: Multi-view Aggregation and Splitting for K-Means Clustering

Description

Aggregation of hard or soft clusters based on multi-view data

Usage

mv_aggregation(
  X,
  mv,
  clustering_init,
  gamma = 2,
  use_mv_weights = TRUE,
  verbose = TRUE
)

Arguments

X	Matrix of multi-view data, where the first view corresponds to the principal data used to obtain the partition or soft clustering in cluster_init
mv	(Optional unless X is a matrix.) If X is a matrix, vector corresponding to the size of each data view. The sum of mv should correspond to the number of columns in X.
clustering_init	Either a vector of available cluster labels (for hard clustering) or a matrix of soft classification labels (For soft clustering)
gamma	Parameter that controls the distribution of view weights. Default value is 2.
use_mv_weights	If TRUE, run algorithm in weighted multi-view mode; if FALSE, the weight for each view is set to be equal. This option is only used for hard clustering.
verbose	If TRUE, provide verbose output

Value

merged_clusters	Matrix providing each pair of merged clusters at each iteration of the algorithm
hclust	Object of class "hclust" to be used for plotting cluster agglomerations
weights	Matrix of dimension v x niterations, where v is the number of views and niterations is the number of successive agglomerations, providing the multi-view weights
criterion	Value taken on by the agglomerative criterion at each iteration

andreamrau/maskmeans documentation built on Nov. 13, 2021, 7:44 a.m.