KmeansQuick: Quick kmeans clustering

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

Quick kmeans clustering

Description

Perform quick kmeans algorithm for data clustering.

Usage

KmeansQuick(features, K)

Arguments

features	matrix of raw data (point by line).
K	number of clusters.

Details

KmeansQuick partition and K number of groups according to kmeans clustering

Value

res.kmeans results obtained from kmeans algorithm.

See Also

KmeansAutoElbow

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 <- KmeansQuick(dat, K=3)

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

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