一、导入标准库

In [1]: # Importing the libraries 导入库 import numpy as np import matplotlib.pyplot as plt import pandas as pd # 使图像能够调整 %matplotlib notebook #中文字体显示 plt.rc('font', family='SimHei', size=8)

二、导入数据

In [2]: dataset = pd.read_csv('Mall_Customers.csv') # 寻找目标用户，根据年收入和购物指数进行聚类分析 dataset Out[2]:  CustomerIDGenreAgeAnnual Income (k$)Spending Score (1-100)01Male19153912Male21158123Female2016634Female23167745Female31174056Female22177667Female3518678Female23189489Male64193910Female3019721011Male6719141112Female3519991213Female5820151314Female2420771415Male3720131516Male2220791617Female3521351718Male2021661819Male5223291920Female3523982021Male3524352122Male2524732223Female462552324Male3125732425Female5428142526Male2928822627Female4528322728Male3528612829Female4029312930Female232987..................170171Male408713171172Male288775172173Male368710173174Male368792174175Female528813175176Female308886176177Male588815177178Male278869178179Male599314179180Male359390180181Female379732181182Female329786182183Male469815183184Female299888184185Female419939185186Male309997186187Female5410124187188Male2810168188189Female4110317189190Female3610385190191Female3410323191192Female3210369192193Male331138193194Female3811391194195Female4712016195196Female3512079196197Female4512628197198Male3212674198199Male3213718199200Male3013783

200 rows × 5 columns

In [4]: X = dataset.iloc[:, 3:5].values # 顾客年收入，顾客购物指数 X Out[4]: array([[ 15, 39], [ 15, 81], [ 16, 6], [ 16, 77], [ 17, 40], [ 17, 76], [ 18, 6], [ 18, 94], [ 19, 3], [ 19, 72], [ 19, 14], [ 19, 99], [ 20, 15], [ 20, 77], [ 20, 13], [ 20, 79], [ 21, 35], [ 21, 66], [ 23, 29], [ 23, 98], [ 24, 35], [ 24, 73], [ 25, 5], [ 25, 73], [ 28, 14], [ 28, 82], [ 28, 32], [ 28, 61], [ 29, 31], [ 29, 87], [ 30, 4], [ 30, 73], [ 33, 4], [ 33, 92], [ 33, 14], [ 33, 81], [ 34, 17], [ 34, 73], [ 37, 26], [ 37, 75], [ 38, 35], [ 38, 92], [ 39, 36], [ 39, 61], [ 39, 28], [ 39, 65], [ 40, 55], [ 40, 47], [ 40, 42], [ 40, 42], [ 42, 52], [ 42, 60], [ 43, 54], [ 43, 60], [ 43, 45], [ 43, 41], [ 44, 50], [ 44, 46], [ 46, 51], [ 46, 46], [ 46, 56], [ 46, 55], [ 47, 52], [ 47, 59], [ 48, 51], [ 48, 59], [ 48, 50], [ 48, 48], [ 48, 59], [ 48, 47], [ 49, 55], [ 49, 42], [ 50, 49], [ 50, 56], [ 54, 47], [ 54, 54], [ 54, 53], [ 54, 48], [ 54, 52], [ 54, 42], [ 54, 51], [ 54, 55], [ 54, 41], [ 54, 44], [ 54, 57], [ 54, 46], [ 57, 58], [ 57, 55], [ 58, 60], [ 58, 46], [ 59, 55], [ 59, 41], [ 60, 49], [ 60, 40], [ 60, 42], [ 60, 52], [ 60, 47], [ 60, 50], [ 61, 42], [ 61, 49], [ 62, 41], [ 62, 48], [ 62, 59], [ 62, 55], [ 62, 56], [ 62, 42], [ 63, 50], [ 63, 46], [ 63, 43], [ 63, 48], [ 63, 52], [ 63, 54], [ 64, 42], [ 64, 46], [ 65, 48], [ 65, 50], [ 65, 43], [ 65, 59], [ 67, 43], [ 67, 57], [ 67, 56], [ 67, 40], [ 69, 58], [ 69, 91], [ 70, 29], [ 70, 77], [ 71, 35], [ 71, 95], [ 71, 11], [ 71, 75], [ 71, 9], [ 71, 75], [ 72, 34], [ 72, 71], [ 73, 5], [ 73, 88], [ 73, 7], [ 73, 73], [ 74, 10], [ 74, 72], [ 75, 5], [ 75, 93], [ 76, 40], [ 76, 87], [ 77, 12], [ 77, 97], [ 77, 36], [ 77, 74], [ 78, 22], [ 78, 90], [ 78, 17], [ 78, 88], [ 78, 20], [ 78, 76], [ 78, 16], [ 78, 89], [ 78, 1], [ 78, 78], [ 78, 1], [ 78, 73], [ 79, 35], [ 79, 83], [ 81, 5], [ 81, 93], [ 85, 26], [ 85, 75], [ 86, 20], [ 86, 95], [ 87, 27], [ 87, 63], [ 87, 13], [ 87, 75], [ 87, 10], [ 87, 92], [ 88, 13], [ 88, 86], [ 88, 15], [ 88, 69], [ 93, 14], [ 93, 90], [ 97, 32], [ 97, 86], [ 98, 15], [ 98, 88], [ 99, 39], [ 99, 97], [101, 24], [101, 68], [103, 17], [103, 85], [103, 23], [103, 69], [113, 8], [113, 91], [120, 16], [120, 79], [126, 28], [126, 74], [137, 18], [137, 83]], dtype=int64)

三、寻找最佳组数（手肘法:找到拐点）

In [12]: from sklearn.cluster import KMeans wcss = [] for i in range(1,11): kmeans = KMeans(n_clusters = i,max_iter = 300,n_init = 10,init= 'k-means++',random_state = 0) kmeans.fit(X) wcss.append(kmeans.inertia_) # 计算组间距离 plt.plot(range(1,11),wcss) plt.title(u'手肘图像') plt.xlabel(u'集群数') plt.ylabel(u'组间距离') plt.show()

从图中可以看出，最佳组数为5

四、K平均聚类算法训练

In [18]: kmeans = KMeans(n_clusters = 5,max_iter = 300,n_init = 10,init= 'k-means++',random_state = 0) # init= 'k-means++' 避免了初始化陷阱 y_kmeans = kmeans.fit_predict(X) print(y_kmeans ) # 打印分类结果 print(kmeans.cluster_centers_)# 打印聚类后的中心点 [4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 1 4 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 0 2 1 2 0 2 0 2 1 2 0 2 0 2 0 2 0 2 1 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2] [[ 88.2 17.11428571] [ 55.2962963 49.51851852] [ 86.53846154 82.12820513] [ 25.72727273 79.36363636] [ 26.30434783 20.91304348]]

五、可视化集群

In [15]: plt.scatter(X[y_kmeans == 0,0],X[y_kmeans == 0,1], s=100, c = 'red',label='Cluster 0') # 理性 plt.scatter(X[y_kmeans == 1,0],X[y_kmeans == 1,1], s=100, c = 'blue',label='Cluster 1') # 标准 plt.scatter(X[y_kmeans == 2,0],X[y_kmeans == 2,1], s=100, c = 'green',label='Cluster 2') # 目标客户 plt.scatter(X[y_kmeans == 3,0],X[y_kmeans == 3,1], s=100, c = 'cyan',label='Cluster 3') # 非理性，小心 plt.scatter(X[y_kmeans == 4,0],X[y_kmeans == 4,1], s=100, c = 'magenta',label='Cluster 4')# 消费敏感用户 plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1], s=300, c = 'yellow',label='Centroids') plt.title(u'顾客群组') plt.xlabel(u'年收入') plt.ylabel(u'购物指数') plt.legend() # 标签显示 plt.show()

六、项目地址

https://coding.net/u/RuoYun/p/Python-of-machine-learning/git/tree/master/00机器学习/4.集群和关联规则学习/1.K平均聚类?public=true