KwaySSSC: Semi-supervised spectral clustering

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

Semi-supervised spectral clustering

Description

Perform semi-supervised spectral clustering thanks to a similarity matrix and constraints sets (according to Wacquet et al., 2013).

Usage

KwaySSSC(
  sim,
  K = 0,
  list.ML = list(),
  list.CNL = list(),
  alphas = seq(from = 0, to = 1, length = 100),
  K.max = 20
)

Arguments

sim	similarity matrix.
K	number of clusters (0 for automatic estimation).
list.ML	list of ML (must-link) constrained pairs.
list.CNL	list of CNL (cannot-link) constrained pairs.
alphas	numeric vector for the weight of constraints considered.
K.max	maximal number of clusters.

Details

KwaySSSC returns a partition obtained by semi-supervised spectral clustering (according to Wacquet et al., 2013)

Value

The function returns a list containing:

alpha	chosen alpha.
label	vector of labels.
medoids	matrix of cluster centers in the space of the K first normalized eigenvectors.
id.med	vector containing the medoids indices.
x	matrix containing, in columns, the eigenvectors of the similarity matrix.
eigen.val	vector containing the eigenvalues of the similarity matrix.
cluster.info	some statistics on each cluster.
K	number of clusters.

References

G. Wacquet, E. Poisson Caillault, D. Hamad, P.-A. Hebert, Constrained spectral embedding for K-way data clustering, Pattern Recognition Letters, 2013, 34 (9), pp.1009-1017.

See Also

computeSemiSupervised, computeCKmeans

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))
           
ML <- list()
ML[[1]] <- c(sel="10",mem="20")
ML[[2]] <- c(sel="60",mem="70")

CNL <- list()
CNL[[1]] <- c(sel="30",mem="80")
CNL[[2]] <- c(sel="90",mem="120")

sim <- computeGaussianSimilarity(dat, 1)
res <- KwaySSSC(sim, K=0, list.ML=ML, list.CNL=CNL)

plot(dat[,1], dat[,2], type = "p", xlab = "x", ylab = "y", 
col = res$label, main = "Constrained spectral clustering")

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