【深度学习基础-17】非监督学习-Hierarchical clustering 层次聚类-python实现

      # -*- coding: utf-8 -*-
      import numpy as np
      from scipy.cluster.hierarchy import dendrogram, linkage, fcluster
      from matplotlib import pyplot as plt
      def hierarchy_cluster(data, method='average', threshold=5.0):
      '''层次聚类
       Arguments:
       data [[0, float, ...], [float, 0, ...]] -- 文档 i 和文档 j 的距离
       Keyword Arguments:
       method {str} -- [linkage的方式： single、complete、average、centroid、median、ward] (default: {'average'})
       threshold {float} -- 聚类簇之间的距离
       Return:
       cluster_number int -- 聚类个数
       cluster [[idx1, idx2,..], [idx3]] -- 每一类下的索引
       '''
       data = np.array(data)
       Z = linkage(data, method=method)
       cluster_assignments = fcluster(Z, threshold, criterion='distance')
       print(type(cluster_assignments))
       num_clusters = cluster_assignments.max()
       indices = get_cluster_indices(cluster_assignments)
       retur
  
 

文章来源: kings.blog.csdn.net，作者：人工智能博士，版权归原作者所有，如需转载，请联系作者。

原文链接：kings.blog.csdn.net/article/details/86528227