KMeansW: C++ Function for Weighted K-Means

In lsbclust: Least-Squares Bilinear Clustering for Three-Way Data

Description

This function does a weighted K-means clustering.

Usage

ComputeMeans(cm, data, weight, nclust)

AssignCluster(data, weight, M, nclust)

KMeansW(nclust, start, data, weight, eps = 1e-08, IterMax = 100L)

Arguments

cm	Numeric vector of class indicators.
data	The concatenated data, with N rows and M columns. Currently, the columns are clustered.
weight	The vector of length nrows(data) with weights with nonnegative elements.
nclust	The number of clusters.
M	Matrix of cluster means.
start	The current cluster membership vector.
eps	Numerical absolute convergence criteria for the K-means.
IterMax	Integer giving the maximum number of iterations allowed for the K-means.

Value

A list with the folowing values.

centers	the nclust by M matrix centers of cluster means.
cluster	vector of length N with cluster memberships.
loss	vector of length IterMax with the first entries containing the loss.
iterations	the number of iterations used (corresponding to the number of nonzero entries in loss)

Examples

set.seed(1)
clustmem <- sample.int(n = 10, size = 100, replace = TRUE)
mat <- rbind(matrix(rnorm(30*4, mean = 3), nrow = 30), 
             matrix(rnorm(30*4, mean = -2), nrow = 30), 
             matrix(rnorm(40*4, mean = 0), nrow = 40))
wt <- runif(100)
testMeans <- lsbclust:::ComputeMeans(cm = clustmem, data = mat, weight = wt, nclust = 3)
testK <- lsbclust:::KMeansW(start = clustmem, data = mat, weight = wt, nclust = 3)

Example output

Loading required package: parallel
Loading required package: ggplot2

lsbclust documentation built on May 1, 2019, 10:27 p.m.