class_docstr: !
  @brief Analyser of cluster ordering diagram.
  @details Using cluster-ordering it is able to connectivity radius for allocation of specified amount of clusters and
            calculate amount of clusters using specified connectivity radius. Cluster-ordering is formed by OPTICS algorithm
            during cluster analysis.
  
  @see optics
docstr: !
  @brief (list) Returns values of dataset cluster ordering.
code: def cluster_ordering(self):
      """!
      @brief (list) Returns values of dataset cluster ordering.
  
      """
      return self.__ordering
  
imports: import math
  import matplotlib.pyplot
  from pyclustering.container.kdtree import kdtree_balanced
  from pyclustering.cluster.encoder import type_encoding
  from pyclustering.utils.color import color
  from pyclustering.core.wrapper import ccore_library
  import pyclustering.core.optics_wrapper
label_desc: Use image node_id 1 for calling the ordering_analyser obj's underlying member method code with example usage: obj.cluster_ordering() and returns: self