predict_KMeans: Prediction function for the k-means

In ClusterR: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering

Prediction function for the k-means

Description

Prediction function for the k-means

Usage

predict_KMeans(data, CENTROIDS, threads = 1, fuzzy = FALSE)

## S3 method for class 'KMeansCluster'
predict(object, newdata, fuzzy = FALSE, threads = 1, ...)

Arguments

data	matrix or data frame
CENTROIDS	a matrix of initial cluster centroids. The rows of the CENTROIDS matrix should be equal to the number of clusters and the columns should be equal to the columns of the data.
threads	an integer specifying the number of cores to run in parallel
fuzzy	either TRUE or FALSE. If TRUE, then probabilities for each cluster will be returned based on the distance between observations and centroids.
object, newdata, ...	arguments for the 'predict' generic

Details

This function takes the data and the output centroids and returns the clusters.

Value

a vector (clusters)

Author(s)

Lampros Mouselimis

Examples

data(dietary_survey_IBS)

dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]

dat = center_scale(dat)

km = KMeans_rcpp(dat, clusters = 2, num_init = 5, max_iters = 100, initializer = 'kmeans++')

pr = predict_KMeans(dat, km$centroids, threads = 1)

ClusterR documentation built on June 22, 2024, 10:28 a.m.