computeKmeans {RclusTool} | R Documentation |
K-means clustering
Description
Perform K-means clustering, dealing with the number of clusters K, automatically or not.
Usage
computeKmeans(x, K = 0, K.max = 20, kmeans.variance.min = 0.95, graph = FALSE)
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. |
Details
computeKmeans performs K-means clustering, dealing with the number of clusters K, automatically or not
Value
res.kmeans results obtained from kmeans algorithm.
See Also
computeUnSupervised
, computeEM
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.kmeans <- computeKmeans(x$features$initial$x, K=0, graph=TRUE)
plot(dat[,1], dat[,2], type = "p", xlab = "x", ylab = "y",
col = res.kmeans$cluster, main = "K-means clustering")
[Package RclusTool version 0.91.6 Index]