computeSemiSupervised: Semi-supervised clustering

Description

Perform semi-supervised clustering based on pairwise constraints, dealing with the number of clusters K, automatically or not.

Usage

computeSemiSupervised(
  data.sample,
  ML,
  CNL,
  K = 0,
  kmax = 20,
  method.name = "Constrained_KM",
  maxIter = 2,
  pca = FALSE,
  pca.nb.dims = 0,
  spec = FALSE,
  use.sampling = FALSE,
  sampling.size.max = 0,
  scaling = FALSE,
  RclusTool.env = initParameters(),
  echo = TRUE
)

Value

The function returns a list containing:

label: vector of labels.
summary: data.frame containing clusters summaries (min, max, sum, average, sd).
nbItems: number of observations.

Arguments

data.sample: list containing features, profiles and clustering results.
ML: list of ML (must-link) constrained pairs (as row.names of features).
CNL: list of CNL (cannot-link) constrained pairs (as row.names of features).
K: number of clusters. If K=0 (default), this number is automatically computed thanks to the Elbow method.
kmax: maximum number of clusters.
method.name: character vector specifying the constrained algorithm to use. Must be 'Constrained_KM' (default) or 'Constrained_SC' (Constrained Spectral Clustering).
maxIter: number of iterations for SemiSupervised algorithm
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 data and intermediate results are stored.
echo: boolean: if FALSE (default), no description printed in the console.

Details

computeSemiSupervised performs semi-supervised clustering based on pairwise constraints, dealing with the number of clusters K, automatically or not

Examples

Run this code

# \donttest{
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)

pairs.abs <- visualizeSampleClustering(x, selection.mode = "pairs", 
		    profile.mode="whole sample", wait.close=TRUE)

res.ckm <- computeSemiSupervised(x, ML=pairs.abs$ML, CNL=pairs.abs$CNL, K=0)
plot(dat[,1], dat[,2], type = "p", xlab = "x", ylab = "y",
    col = res.ckm$label, main = "Constrained K-means clustering")


# }