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 clustering result representation type that indicate how clusters are encoded.
  
  @return (type_encoding) Clustering result representation.
  
  @see get_clusters()
docstr_tok: ["!","@","brief","Returns","clustering","result","representation","type","that","indicate","how","clusters","are","encoded",".","@","return","(","type_encoding",")","Clustering","result","representation",".","@","see","get_clusters","(",")"]
code: def get_cluster_encoding(self):
      """!
      @brief Returns clustering result representation type that indicate how clusters are encoded.
  
      @return (type_encoding) Clustering result representation.
  
      @see get_clusters()
  
      """
  
      return type_encoding.CLUSTER_INDEX_LIST_SEPARATION
  
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 6 for calling the clarans obj's underlying member method code with example usage: obj.get_cluster_encoding() and returns: type_encoding