clusterProjDivisive: Divisive (hierarchical) clustering on the projective space

In pws3141/clusterICA: Approximate Independent Component Anaylsis

Description

Creates clusters of points on the projective space using divisive k-means clustering

Usage

clusterProjDivisive(X, tol, iter.max = 100)

Arguments

X	the data belonging to the projective space
tol	the tolerance that when reached, stops increasing the number of clusters. At each step, the (change in wcss) / (original wcss) must be above this tolerance. In general, as the tolerance decreases, the number of clusters in the output increases.
iter.max	the maximum number of iterations

Value

A list with the following components:

• c Vector of real numbers from 1 to K representing the cluster that the corresponding X value belongs to.

• sseIndividual Vector of the within sum-of-squares for each cluster

• rmse The total within-cluster root mean-squared-error for the obtained cluster

• rmseSequence The change in total within-cluster root mean-squared-error for each iteration of the function

Author(s)

Paul Smith, [e-mail]

See Also

clusterProjKmeans

Examples

n1 <- 37; n2 <- 19
x1 <- rnorm(n1, 6); y1 <- rnorm(n1, 0); z1 <- rnorm(n1, 0, 0.1)
x2 <- rnorm(n2, 8); y2 <- rnorm(n2, 8); z2 <- rnorm(n2, 0, 0.1)
X <- rbind(cbind(x1, y1, z1), cbind(x2, y2, z2)) * sample(c(-1, 1), size=n1+n2, replace=TRUE)
X <- X / sqrt(rowSums(X^2))
(c <- clusterProjDivisive(X=X, tol=0.1))

pws3141/clusterICA documentation built on July 14, 2020, 5:04 a.m.