onekmeans {simuclustfactor}

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

Oti EU, Olusola MO, Eze FC, Enogwe SU (2021). “Comprehensive Review of K-Means Clustering Algorithms.” International Journal of Advances in Scientific Research and Engineering (IJASRE), ISSN:2454-8006, DOI: 10.31695/IJASRE, 7(8), 64–69. doi:10.31695/IJASRE.2021.34050, https://ijasre.net/index.php/ijasre/article/view/1301.

Examples

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