class_docstr: Combining multiple clusterings using evidence accumulation (EAC) first
  builds similarity matrix for each base clustering to model the similarity
  among the cluster assignment among each sample. After the similarity
  matrices are aggregated, a hierarchical clustering is built on it. See
  :cite:`fred2005combining` for details.
  
  Parameters
  ----------
  base_estimators : list or numpy array of shape (n_estimators,)
      A list of base estimators. Estimators must have a `labels_`
      attribute once fitted. Sklearn clustering estimators are recommended.
  
  n_clusters : int, optional (default=8)
      The number of clusters.
  
  linkage_method : str, optional (default='single')
      The linkage method to use (single, complete, average,
      weighted, median centroid, ward). See
      https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html
      for more information.
  
  weights : numpy array of shape (n_estimators,)
      Estimator weights. May be used after the alignment.
  
  pre_fitted : bool, optional (default=False)
      Whether the base estimators are trained. If True, `fit`
      process may be skipped.
  
  Attributes
  ----------
  labels_ : int
      The predicted label of the fitted data.
  
  Z_ : numpy array
      The linkage matrix encoding the hierarchical clustering. This can be
      used to plot dendrogram using scipy.
code: def __init__(
      self,
      base_estimators,
      n_clusters,
      linkage_method="single",
      weights=None,
      pre_fitted=False,
  ):
      super(EAC, self).__init__(
          base_estimators=base_estimators, pre_fitted=pre_fitted
      )
  
      check_parameter(n_clusters, low=2, param_name="n_clusters")
      self.n_clusters = n_clusters
  
      # set estimator weights
      self._set_weights(weights)
  
      self.linkage_method = linkage_method
  
file_path: combo/combo/models/cluster_eac.py
imports: import warnings
  import numpy
  from scipy.cluster.hierarchy import fcluster
  from scipy.cluster.hierarchy import linkage
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from pyod.utils.utility import check_parameter
  from .base import BaseAggregator
label_desc: Use image node_id 1 to create a new EAC object from inherited base classes: BaseAggregator with example: obj = EAC(base_estimators, n_clusters, linkage_method, weights, pre_fitted)