cleanUp_partition: Remove small clusters (i.e. unclassified observations for...

In LilithF/doMIsaul: Do Multiple Imputation-Based Semi-Supervised and Unsupervised Learning

Description

Remove small clusters (i.e. unclassified observations for which no consensus was obtained)

Usage

cleanUp_partition(
  partition,
  min.cluster.size = 10,
  level.order = NULL,
  Unclassified = c(NA, "Unclassified")
)

Arguments

partition	the partition to clean (vector).
min.cluster.size	Minimum cluster size (i.e., smaller clusters will be discarded)
level.order	optional. If you supply a variable the cluster levels will be ordinated according to the mean values for the variable
Unclassified	string for the label of the unclassified observations. defaults value is NA.

Value

The cleaned up partition (factor).

Examples

part <- factor(kmeans(iris[, 1:4], 8)$cluster)
summary(part)
part.clean <- cleanUp_partition(part, Unclassified = "Unclassified")

LilithF/doMIsaul documentation built on Dec. 17, 2021, 12:08 a.m.