predict_Medoids: Predictions for the Medoid functions

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

Predictions for the Medoid functions

Description

Predictions for the Medoid functions

Usage

predict_Medoids(
  data,
  MEDOIDS = NULL,
  distance_metric = "euclidean",
  fuzzy = FALSE,
  minkowski_p = 1,
  threads = 1
)

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

Arguments

data	matrix or data frame
MEDOIDS	a matrix of initial cluster medoids (data observations). The rows of the MEDOIDS matrix should be equal to the number of clusters and the columns of the MEDOIDS matrix should be equal to the columns of the data.
distance_metric	a string specifying the distance method. One of, euclidean, manhattan, chebyshev, canberra, braycurtis, pearson_correlation, simple_matching_coefficient, minkowski, hamming, jaccard_coefficient, Rao_coefficient, mahalanobis, cosine
fuzzy	either TRUE or FALSE. If TRUE, then probabilities for each cluster will be returned based on the distance between observations and medoids.
minkowski_p	a numeric value specifying the minkowski parameter in case that distance_metric = "minkowski"
threads	an integer specifying the number of cores to run in parallel. Openmp will be utilized to parallelize the number of initializations (num_init)
object, newdata, ...	arguments for the 'predict' generic

Value

a list with the following attributes will be returned : clusters, fuzzy_clusters (if fuzzy = TRUE), dissimilarity.

Author(s)

Lampros Mouselimis

Examples

data(dietary_survey_IBS)

dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]

dat = center_scale(dat)

cm = Cluster_Medoids(dat, clusters = 3, distance_metric = 'euclidean', swap_phase = TRUE)

pm = predict_Medoids(dat, MEDOIDS = cm$medoids, 'euclidean', fuzzy = TRUE)

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