computeUnSupervised: Unsupervised clustering

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

Unsupervised clustering

Description

Perform unsupervised clustering, dealing with the number of clusters K, automatically or not.

Usage

computeUnSupervised(
  data.sample,
  K = 0,
  method.name = "K-means",
  pca = FALSE,
  pca.nb.dims = 0,
  spec = FALSE,
  use.sampling = FALSE,
  sampling.size.max = 0,
  scaling = FALSE,
  RclusTool.env = initParameters(),
  echo = FALSE
)

Arguments

data.sample	list containing features, profiles and clustering results.
K	number of clusters. If K=0 (default), this number is automatically computed thanks to the Elbow method.
method.name	character vector specifying the constrained algorithm to use. Must be 'K-means' (default), 'EM' (Expectation-Maximization), 'Spectral', 'HC' (Hierarchical Clustering) or 'PAM' (Partitioning Around Medoids).
pca	boolean: if TRUE, Principal Components Analysis is applied to reduce the data space.
pca.nb.dims	number of principal components kept. If pca.nb.dims=0, this number is computed automatically.
spec	boolean: if TRUE, spectral embedding is applied to reduce the data space.
use.sampling	boolean: if FALSE (default), data sampling is not used.
sampling.size.max	numeric: maximal size of the sampling set.
scaling	boolean: if TRUE, scaling is applied.
RclusTool.env	environment in which all global parameters, raw data and results are stored.
echo	boolean: if FALSE (default), no description printed in the console.

Details

computeUnSupervised performs unsupervised clustering, dealing with the number of clusters K, automatically or not

Value

data.sample list containing features, profiles and updated clustering results (with vector of labels and clusters summaries).

See Also

computeKmeans, computeEM, spectralClustering, computePcaSample, computeSpectralEmbeddingSample

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))
tf <- tempfile()
write.table(dat, tf, sep=",", dec=".")
x <- importSample(file.features=tf)

x <- computeUnSupervised(x, K=0, pca=TRUE, echo=TRUE)
label <- x$clustering[["K-means_pca"]]$label
plot(dat[,1], dat[,2], type = "p", xlab = "x", ylab = "y", 
    col = label, main = "K-means clustering")

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