class_docstr: Cluster the label space regarding the algorithm of balancedkMeans, used by HOMER
docstr: Initializes the clusterer
  
  Attributes
  ----------
  k: int
          Number of partitions to be made to the label-space
  it: int
          Number of iterations for the algorithm to find the best neighbours
docstr_tok: ["Initializes","the","clusterer","Attributes","--","--","--","--","--","k",":","int","Number","of","partitions","to","be","made","to","the","label-space","it",":","int","Number","of","iterations","for","the","algorithm","to","find","the","best","neighbours"]
code: def __init__(self, k=None, it=None):
      """Initializes the clusterer
  
      Attributes
      ----------
      k: int
              Number of partitions to be made to the label-space
      it: int
              Number of iterations for the algorithm to find the best neighbours
      """
      super(BalancedKMeansClusterer, self).__init__()
      self.k = k
      self.it = it
  
file_path: scikit-multilearn/skmultilearn/cluster/balancedkmeans.py
imports: from __future__ import absolute_import
  import numpy
  import random
  from .base import LabelSpaceClustererBase
  from .helpers import _euclidean_distance, _recalculateCenters, _countNumberOfAparitions
label_desc: Use image node_id 1 to create a new BalancedKMeansClusterer object from inherited base classes: LabelSpaceClustererBase with example: obj = BalancedKMeansClusterer(k, it)