scidb (version 1.2-0)

kmeans: kmeans

Description

Basic kmeans clustering using Lloyd's algorithm

Usage

"kmeans"(x,  centers, iter.max=30, nstart=1, algorithm="Lloyd")

Arguments

x

A scidb object representing a matrix.

centers

the number of clusters

iter.max

the maximum number of iterations allowed

nstart

Not (yet) used by this implementation

algorithm

Not (yet) used by this implementation (always Lloyd's)

Value

A list of class kmeans.

See Also

kmeans

Examples

## Not run: 
# # Let's generate data with obvious clusters.
# set.seed(1)
# x <- c(x=rnorm(50),rnorm(50)+3);y = c(rnorm(50),rnorm(50)+3)
# A <- cbind(x,y)
# # Plot the data (try to set the aspect ratio square)
# # Let a be an m x n matrix.
# # Each row of the matrix A is a point in an n-dimensional Euclidean space.
# library("scidb")
# scidbconnect()
# A = as.scidb(A)
# k = kmeans(A, 2, 10)
# plot(A[], asp=1,xlab="x",ylab="y")
# points(A[k$cluster %==%0, ][], pch=19,col=4)
# ## End(Not run)