k-means可视化

from sklearn.cluster import KMeans
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
X=np.array(value)
X_max=np.max(X)
X_min=np.min(X)
X=(X-X_min)/(X_max-X_min)
X=np.vstack(X).reshape(-1,1)

model = KMeans(        n_clusters=9,
                        init='k-means++',
                        n_init=10,
                        max_iter=300,
                        random_state=0      )
model.fit(X)

X_out = pd.DataFrame(X,index=model.labels_) #用X和对应的聚类标签值组成新的dataframe
X_out_center = pd.DataFrame(model.cluster_centers_) #用计算得到的聚类中心组成dataframe
X_outwithcenter=X_out.append(X_out_center)#将X_out_center放入到X_out中，一并tsne，不能单独tsne

将X_outwithcenter进行降维，并进行可视化：

#用TSNE进行数据降维并展示聚类结果
from sklearn.manifold import TSNE
tsne = TSNE()
tsne.fit_transform(X_outwithcenter) #进行数据降维,并返回结果
X_tsne = pd.DataFrame(tsne.embedding_, index = X_outwithcenter.index)
#将index化成原本的数据的index，tsne后index会变化

import matplotlib.pyplot as plt
#根据类别分割数据后，画图
d0 = X_tsne[X_tsne.index == 0]
plt.scatter(d0[0], d0[1],c='lightgreen',
         		marker='o'				)
d1 = X_tsne[X_tsne.index == 1]
plt.scatter(d1[0], d1[1], c='orange',
         		marker='o'				)
d2 = X_tsne[X_tsne.index == 2]
plt.scatter(d2[0], d2[1], c='lightblue',
         		marker='o'				)
d3 = X_tsne[X_tsne.index == 3]
plt.scatter(d3[0], d3[1], c='black',
         		marker='o'				)
d4 = X_tsne[X_tsne.index == 4]
plt.scatter(d4[0], d4[1], c='chocolate',
         		marker='o'				)
d5 = X_tsne[X_tsne.index == 5]
plt.scatter(d5[0], d5[1], c='cyan',
         		marker='o'				)
d6 = X_tsne[X_tsne.index == 6]
plt.scatter(d6[0], d6[1], c='azure',
         		marker='o'				)
d7 = X_tsne[X_tsne.index == 7]
plt.scatter(d7[0], d7[1], c='bisque',
         		marker='o'				)
d8 = X_tsne[X_tsne.index == 8]
plt.scatter(d8[0], d8[1], c='burlywood',
         		marker='o'				)


#取中心点，由于分成了9类，所以X_tsne尾部的9条数据是后来加入的中心点信息
d = X_tsne.tail(9)
plt.scatter(d[0], d[1], c='yellow',s=150,
         		marker='*'				)
plt.show()