class_docstr: !
  @brief Class represents clustering algorithm CLARANS (a method for clustering objects for spatial data mining).
class_docstr_tok: ["!","@","brief","Class","represents","clustering","algorithm","CLARANS","(","a","method","for","clustering","objects","for","spatial","data","mining",")","."]
docstr: !
  @brief Returns list of medoids of allocated clusters.
  
  @see process()
  @see get_clusters()
docstr_tok: ["!","@","brief","Returns","list","of","medoids","of","allocated","clusters",".","@","see","process","(",")","@","see","get_clusters","(",")"]
code: def get_medoids(self):
      """!
      @brief Returns list of medoids of allocated clusters.
  
      @see process()
      @see get_clusters()
  
      """
  
      return self.__optimal_medoids
  
file_path: pyclustering/pyclustering/cluster/clarans.py
imports: import random
  from pyclustering.cluster.encoder import type_encoding
  from pyclustering.utils import euclidean_distance_square
label_desc: Use image node_id 5 for calling the clarans obj's underlying member method code with example usage: obj.get_medoids() and returns: self