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
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)