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Cluster Center Initialization

Source: R/init.centers.R
init.centers.Rd

init.centers automatically initializes the centers of the clusters before running the Cross-Entropy Clustering algorithm.

Usage

init.centers(x, k, method = c("kmeans++", "random"))

Arguments

x

A numeric matrix of data. Each row corresponds to a distinct observation; each column corresponds to a distinct variable/dimension. It must not contain NA values.

k

An integer indicating the number of cluster centers to initialize.

method

A character string indicating the initialization method to use. It can take the following values:

"kmeans++":

the centers are selected using the k-means++ algorithm.

"random":

the centers are randomly selected among the values in x

Value

A matrix with k rows and ncol(x) columns.

References

Arthur, D., & Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035.

Examples

## See the examples provided with the cec() function.