computeEM: Expectation-Maximization clustering

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

Expectation-Maximization clustering

Description

Perform Expectation-Maximization clustering, dealing with the number of clusters K, automatically or not.

Usage

computeEM(
  x,
  K = 0,
  K.max = 20,
  kmeans.variance.min = 0.95,
  graph = FALSE,
  Mclust.options = list()
)

Arguments

x	matrix of raw data (point by line).
K	number of clusters. If K=0 (default), this number is automatically computed thanks to the Elbow method.
K.max	maximal number of clusters (K.Max=20 by default).
kmeans.variance.min	elbow method cumulative explained variance > criteria to stop K-search.
graph	boolean: if TRUE, figures for total of within-class inertia and explained variance are plotted.
Mclust.options	list of default parameters values for the function Mclust.

Details

computeEM performs Expectation-Maximization clustering, dealing with the number of clusters K, automatically or not

Value

res.EM results obtained from Mclust algorithm.

See Also

computeUnSupervised, computeKmeans

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)
res.em <- computeEM(x$features$initial$x, K=0, graph=TRUE)
plot(dat[,1], dat[,2], type = "p", xlab = "x", ylab = "y", 
col = res.em$classification, main = "EM clustering")

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