init.centers: Cluster Center Initialization

In CEC: Cross-Entropy Clustering

Cluster Center Initialization

Description

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.

CEC documentation built on Dec. 28, 2022, 2:53 a.m.