clusterDensity: Clusters density computation

In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

Clusters density computation

Description

Save density summaries results.

Usage

clusterDensity(
  data.sample,
  label,
  space,
  features.to.keep = colnames(data.sample$features[[space]]$x)
)

Arguments

data.sample	list containing features, profiles and clustering results.
label	vector of labels.
space	space in which is the feature to deal with.
features.to.keep	vector of features names on which the summaries are computed.

Details

clusterDensity computes the clusters density from a clustering result.

Value

out data.frame containing the density summaries.

Examples

dat <- rbind(matrix(rnorm(100, mean = 0, sd = 0.3), ncol = 2), 
             matrix(rnorm(100, mean = 2, sd = 0.3), ncol = 2), 
             matrix(rnorm(100, mean = 4, sd = 0.3), ncol = 2))
tf1 <- tempfile()
write.table(dat, tf1, sep=",", dec=".")

x <- importSample(file.features=tf1)
x <- computeUnSupervised(x, K=3, method.name="K-means")

label<-x[["clustering"]][["K-means_preprocessed"]][["label"]]

cluster.density <- clusterDensity(x, label, "preprocessed", features.to.keep='V1')

RclusTool documentation built on Aug. 29, 2022, 9:07 a.m.