Predictions for the Medoid functions

Source: R/clustering_functions.R

Predictions for the Medoid functions

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

# S3 method for 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

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)