ockc: Order Contrained Solutions in k-Means Clustering

In ockc: Order Constrained Solutions in k-Means Clustering

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(s)

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.

See Also

kcca

Examples

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

ockc documentation built on Dec. 28, 2022, 2:18 a.m.