K-Means聚类算法实践

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                原文链接：https://blog.youkuaiyun.com/itnerd/article/details/83420139

首先生成原始数据点：
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

def gen_clusters():
mean1 = [0,0]
cov1 = [[1,0],[0,10]]
data = np.random.multivariate_normal(mean1,cov1,100)

mean2 = [10,10]
cov2 = [[10,0],[0,1]]
data = np.append(data,
                 np.random.multivariate_normal(mean2,cov2,100),
                 0)

mean3 = [10,0]
cov3 = [[3,0],[0,4]]
data = np.append(data,
                 np.random.multivariate_normal(mean3,cov3,100),
                 0)

return np.round(data,4)

def show_scatter(data,colors):
x,y = data.T
plt.scatter(x,y,c=colors)
plt.axis()
plt.title(“scatter”)
plt.xlabel(“x”)
plt.ylabel(“y”)
plt.show()

data = gen_clusters()
show_scatter(data,‘k’)

使用 K-Means 方法聚类：
// 初始化参数为 k-means++
estimator = KMeans(init=‘k-means++’, n_clusters=3, n_init=3)
estimator.fit(data)
label2color = [‘r’,‘g’,‘b’]
colors = [label2color[i] for i in estimator.labels_]
show_scatter(data,colors)

初始化参数为 k-means++

初始化参数为 random

聚类中心：
centroids = estimator.cluster_centers_
print(centroids)
#高斯中心
[[ 9.8040697 -0.01635758] ==>(10,0)
[ 10.16384455 10.03000693] ==>(10,10)
[ -0.038093 0.13642 ]] ==>(0,0)