onekmeans: One-run of the K-means clustering technique

In simuclustfactor: Simultaneous Clustering and Factorial Decomposition of Three-Way Datasets

One-run of the K-means clustering technique

Description

Initializes centroids based on a given membership function matrix or randomly. Iterate once over the input data to update the membership function matrix assigning objects to the closest centroids.

Usage

onekmeans(Y_i_qr, G, U_i_g = NULL, seed = NULL)

Arguments

Y_i_qr	Input data to group/cluster.
G	Number of clusters to find.
U_i_g	Initial membership matrix for the I objects.
seed	Seed for random values generation.

Value

updated membership matrix U_i_g.

References

\insertRef

k_meansMethodssimuclustfactor

Examples

X_i_jk = generate_dataset(seed=0)$X_i_jk
onekmeans(X_i_jk, G=5)

simuclustfactor documentation built on Oct. 18, 2022, 9:07 a.m.