本篇文章为大家展示了K-means算法怎么在python中使用，内容简明扼要并且容易理解，绝对能使你眼前一亮，通过这篇文章的详细介绍希望你能有所收获。
K-Means算法进行聚类分析
km = KMeans(n_clusters = 3) km.fit(X) centers = km.cluster_centers_ print(centers)
三个簇的中心点坐标为：
[[5.006 3.428 ]
[6.81276596 3.07446809]
[5.77358491 2.69245283]]
比较一下K-Means聚类结果和实际样本之间的差别：
计算
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predicted_labels = km.labels_
fig, axes = plt.subplots(1, 2, figsize=(16,8))
axes[0].scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,
edgecolor='k', s=150)
axes[1].scatter(X[:, 0], X[:, 1], c=predicted_labels, cmap=plt.cm.Set1,
edgecolor='k', s=150)
axes[0].set_xlabel('Sepal length', fontsize=16)
axes[0].set_ylabel('Sepal width', fontsize=16)
axes[1].set_xlabel('Sepal length', fontsize=16)
axes[1].set_ylabel('Sepal width', fontsize=16)
axes[0].tick_params(direction='in', length=10, width=5, colors='k', labelsize=20)
axes[1].tick_params(direction='in', length=10, width=5, colors='k', labelsize=20)
axes[0].set_title('Actual', fontsize=18)
axes[1].set_title('Predicted', fontsize=18)k-means算法实例扩展内容：
# -*- coding: utf-8 -*-
"""Excercise 9.4"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
import random
data = pd.read_csv(filepath_or_buffer = '../dataset/watermelon4.0.csv', sep = ',')[["密度","含糖率"]].values
########################################## K-means #######################################
k = int(sys.argv[1])
#Randomly choose k samples from data as mean vectors
mean_vectors = random.sample(data,k)
def dist(p1,p2):
return np.sqrt(sum((p1-p2)*(p1-p2)))
while True:
print mean_vectors
clusters = map ((lambda x:[x]), mean_vectors)
for sample in data:
distances = map((lambda m: dist(sample,m)), mean_vectors)
min_index = distances.index(min(distances))
clusters[min_index].append(sample)
new_mean_vectors = []
for c,v in zip(clusters,mean_vectors):
new_mean_vector = sum(c)/len(c)
#If the difference betweenthe new mean vector and the old mean vector is less than 0.0001
#then do not updata the mean vector
if all(np.divide((new_mean_vector-v),v) < np.array([0.0001,0.0001]) ):
new_mean_vectors.append(v)
else:
new_mean_vectors.append(new_mean_vector)
if np.array_equal(mean_vectors,new_mean_vectors):
break
else:
mean_vectors = new_mean_vectors
#Show the clustering result
total_colors = ['r','y','g','b','c','m','k']
colors = random.sample(total_colors,k)
for cluster,color in zip(clusters,colors):
density = map(lambda arr:arr[0],cluster)
sugar_content = map(lambda arr:arr[1],cluster)
plt.scatter(density,sugar_content,c = color)
plt.show()上述内容就是K-means算法怎么在python中使用，你们学到知识或技能了吗？如果还想学到更多技能或者丰富自己的知识储备，欢迎关注亿速云行业资讯频道。
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