KmeansAutoElbow: Kmeans clustering with automatic estimation of number of...

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

Kmeans clustering with automatic estimation of number of clusters

Description

Perform Elbow method and kmeans algorithm for the automatic estimation of the number of clusters and data clustering.

Usage

KmeansAutoElbow(
  features,
  Kmax,
  StopCriteria = 0.99,
  graph = FALSE,
  Elbow = TRUE
)

Arguments

features	matrix of raw data (point by line).
Kmax	maximum number of clusters.
StopCriteria	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.
Elbow	boolean: if TRUE, Elbow method is used for finding the knee point of a curve.

Details

KmeansAutoElbow return partition and K number of groups according to kmeans clustering and Elbow method

Value

The function returns a list containing:

K	number of clusters obtained by Elbow method.
res.kmeans	results obtained from kmeans algorithm.

See Also

KmeansQuick

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))
           
res <- KmeansAutoElbow(dat, Kmax=20, graph=TRUE)

plot(dat[,1], dat[,2], type = "p", xlab = "x", ylab = "y", 
	col = res$res.kmeans$cluster, main = "K-means clustering")

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