python实现层次聚类的方法

from scipy.cluster.hierarchy import linkage, dendrogram, fcluster
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np


data = pd.read_csv('data/WBClust2013.csv')
data.pop('Pop')
# data.pop('RuralWater')
# data.pop('CellPhone')
# data.pop('LifeExp')
data = data[:20]
country = list(data['Country'])
data.pop('Country')

# 以下代码为仅使用层次聚类

plt.figure(figsize=(9, 7))
plt.title("original data")
mergings = linkage(data, method='average')
# print(mergings)
dendrogram(mergings, labels=country, leaf_rotation=45, leaf_font_size=8)
plt.show()
Z = linkage(data, method='average')
print(Z)
cluster_assignments = fcluster(Z, t=3.0, criterion='maxclust')
print(cluster_assignments)
for i in range(1, 4):
    print('cluster', i, ':')
    num = 1
    for index, value in enumerate(cluster_assignments):
        if value == i:
            if num % 5 == 0:
                print()
            num += 1
            print(country[index], end='  ')
    print()

# 以下代码为加入PCA进行对比
class myPCA():

    def __init__(self, X, d=2):
        self.X = X
        self.d = d

    def mean_center(self, data):
        """
        去中心化
        :param data: data sets
        :return:
        """
        n, m = data.shape
        for i in range(m):
            aver = np.sum(self.X[:, i])/n
            x = np.tile(aver, (1, n))
            self.X[:, i] = self.X[:, i]-x

    def runPCA(self):

        # 计算协方差矩阵，得到特征值，特征向量
        S = np.dot(self.X.T, self.X)
        S_val, S_victors = np.linalg.eig(S)
        index = np.argsort(-S_val)[0:self.d]
        Y = S_victors[:, index]
        # 得到输出样本集
        Y = np.dot(self.X, Y)
        return Y

# data_for_pca = np.array(data)
# pcaObject=myPCA(data_for_pca,d=2)
# pcaObject.mean_center(data_for_pca)
# res=pcaObject.runPCA()

# plt.figure(figsize=(9, 7))
# plt.title("after pca")
# mergings = linkage(res,method='average')
# print(mergings)
# dendrogram(mergings,labels=country,leaf_rotation=45,leaf_font_size=8)
# plt.show()
# Z = linkage(res, method='average')
# print(Z)
# cluster_assignments = fcluster(Z, t=3.0, criterion='maxclust')
# print(cluster_assignments)
# for i in range(1,4):
#     print('cluster', i, ':')
#     num = 1
#     for index, value in enumerate(cluster_assignments):
#         if value == i:
#             if num % 5 ==0:
#                 print()
#             num+=1
#             print(country[index],end='  ')
#     print()

cluster 1 :
China  United States  Indonesia  Brazil  
Russian Federation  Japan  Mexico  Philippines  Vietnam  
Egypt, Arab Rep.  Germany  Turkey  Thailand  France  
United Kingdom  
cluster 2 :
India  Pakistan  Nigeria  Bangladesh  
cluster 3 :
Ethiopia