(编辑：jimmy 日期: 2024/12/27 浏览：2)
能够学习和掌握编程，最好的学习方式，就是去掌握基本的使用技巧，再多的概念意义，总归都是为了使用服务的，K-means算法又叫K-均值算法，是非监督学习中的聚类算法。主要有三个元素，其中N是元素个数，x表示元素，c(j)表示第j簇的质心，下面就使用方式给大家简单介绍实例使用。
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：
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()