RclusTool (version 0.91)

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 = F,
  pca.nb.dims = 0, spec = F, use.sampling = FALSE,
  sampling.size.max = 0, scaling = F,
  RclusTool.env = initParameters(), echo = T)

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.

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.

Details

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

See Also

computeCKmeans, computeCSC, KwaySSSC

Examples

# NOT RUN {
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, dir.save=tempdir())

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")


# }
# NOT RUN {
# }