ockc: Order Contrained Solutions in k-Means Clustering

Description

Calculates an order constrained clustering solution (default k-means) on a data matrix.

Usage

ockc(x, k, family = kccaFamily("kmeans"), order = NULL, control = NULL,
     save.data = FALSE, multicore = FALSE, ...)

Arguments

x: A numeric matrix of data.
k: An integer vector of number of clusters. For each element of k a clustering solution is computed (reusage of intermediate results makes this more efficient than individual calls of ockc).
family: Object of class kccaFamily.
order: Order restriction of x. If NULL an order is caluclated with seriate from package seriation
control: An object of class flexclustControl.
save.data: Save a copy of x in the return object?
multicore: Use parallelization, if available. For examples and additional documentation see bootFlexclust.
...: Additional options for seriate for order calculation.

Author

Sebastian Krey, Friedrich Leisch, Sebastian Hoffmeister

References

Steinley, D. and Hubert, L. (2008). Order-Constrained Solutions in K-Means Clustering: Even Better Than Being Globally Optimal. Psychometrika, 73 (4), pp. 647-664.

Examples

Run this code

x <- rbind(cbind(rnorm(10, mean=0), rnorm(10, mean=0,), rnorm(10, mean=0)),
           cbind(rnorm(10, mean=10), rnorm(10, mean=10), rnorm(10, mean=0)),
           cbind(rnorm(10, mean=10), rnorm(10, mean=0), rnorm(10, mean=10)),
           cbind(rnorm(10, mean=10), rnorm(10, mean=10), rnorm(10, mean=10))
           )

res <- ockc(x, k=4, nboot=4, order=c(1:10, 21:40, 11:20))
res