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 allocated clusters by the algorithm.
  
  @remark Allocated clusters can be returned only after data processing (use method process()), otherwise empty list is returned.
  
  @return (list) List of allocated clusters, each cluster contains indexes of objects in list of data.
  
  @see process()
  @see get_medoids()
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code: def get_clusters(self):
      """!
      @brief Returns allocated clusters by the algorithm.
  
      @remark Allocated clusters can be returned only after data processing (use method process()), otherwise empty list is returned.
  
      @return (list) List of allocated clusters, each cluster contains indexes of objects in list of data.
  
      @see process()
      @see get_medoids()
  
      """
  
      return self.__clusters
  
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 4 for calling the clarans obj's underlying member method code with example usage: obj.get_clusters() and returns: self