【Python-ML】SKlearn库层次聚类凝聚AgglomerativeClustering模型

# -*- coding: utf-8 -*- ''' Created on 2018年1月25日 @author: Jason.F @summary: 无监督聚类学习-层次聚类(hierarchical clustering)，自下向上的凝聚和自顶向下的分裂两种方法。 ''' import pandas as pd import numpy as np import matplotlib.pyplot as plt from scipy.spatial.distance import pdist,squareform from scipy.cluster.hierarchy import linkage from scipy.cluster.hierarchy import dendrogram from sklearn.cluster import AgglomerativeClustering np.random.seed(123) variables = ['X','Y','Z'] labels=['ID_0','ID_1','ID_2','ID_3','ID_4'] X=np.random.random_sample([5,3])*10 #层次聚类树 df = pd.DataFrame(X,columns=variables,index=labels) print (df) #计算距离关联矩阵，两两样本间的欧式距离 #row_dist = pd.DataFrame(squareform(pdist(df,metric='euclidean')),columns=labels,index=labels) #print (row_dist) #print (help(linkage)) row_clusters = linkage(pdist(df,metric='euclidean'),method='complete')#使用抽秘籍距离矩阵 #row_clusters = linkage(df.values,method='complete',metric='euclidean') print (pd.DataFrame(row_clusters,columns=['row label1','row label2','distance','no. of items in clust.'],index=['cluster %d'%(i+1) for i in range(row_clusters.shape[0])])) #层次聚类树 row_dendr = dendrogram(row_clusters,labels=labels) plt.tight_layout() plt.ylabel('Euclidean distance') plt.show() #层次聚类热度图 fig =plt.figure(figsize=(8,8)) axd =fig.add_axes([0.09,0.1,0.2,0.6]) row_dendr = dendrogram(row_clusters,orientation='right') df_rowclust = df.ix[row_dendr['leaves'][::-1]] axm = fig.add_axes([0.23,0.1,0.6,0.6]) cax = axm.matshow(df_rowclust,interpolation='nearest',cmap='hot_r') axd.set_xticks([]) axd.set_yticks([]) for i in axd.spines.values():i.set_visible(False) fig.colorbar(cax) axm.set_xticklabels(['']+list(df_rowclust.columns)) axm.set_yticklabels(['']+list(df_rowclust.index)) plt.show()#凝聚层次聚类，应用对层次聚类树剪枝 ac=AgglomerativeClustering(n_clusters=2,affinity='euclidean',linkage='complete') labels = ac.fit_predict(X) print ('cluster labels:%s'%labels)

结果：

X Y Z ID_0 6.964692 2.861393 2.268515 ID_1 5.513148 7.194690 4.231065 ID_2 9.807642 6.848297 4.809319 ID_3 3.921175 3.431780 7.290497 ID_4 4.385722 0.596779 3.980443row label1 row label2 distance no. of items in clust. cluster 1 0.0 4.0 3.835396 2.0 cluster 2 1.0 2.0 4.347073 2.0 cluster 3 3.0 5.0 5.899885 3.0 cluster 4 6.0 7.0 8.316594 5.0 cluster labels:[0 1 1 0 0]

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