编辑：K 表示聚类并找到最接近质心的点

Question

我正在尝试根据以下列中的信息将 k 均值应用于集群参与者

Actors              Movies  TvGuest Awards  Shorts  Special LiveShows
Robert De Niro         111      2     6       0        0       0
Jack Nicholson          70      2     4       0        5       0
Marlon Brando           64      2     5       0        0       28
Denzel Washington       25      2     3      24        0       0
Katharine Hepburn       90      1     2       0        0       0
Humphrey Bogart        105      2     1       0        0       52
Meryl Streep            27      2     2       5        0       0
Daniel Day-Lewis        90      2     1       0       71      22
Sidney Poitier          63      2     3       0        0       0
Clark Gable             34      2     4       0        3       0 
Ingrid Bergman          22      2     2       3        0       4
Tom Hanks               82     11     6      21       11      22

#began by scaling my data
X = StandardScaler().fit_transform(data)

#used an elbow plot to find optimal k value  
sum_of_squared_distances = []
K = range(1,15)
for k in K:
    k_means = KMeans(n_clusters=k)
    model = k_means.fit(X)
    sum_of_squared_distances.append(k_means.inertia_)
plt.plot(K, sum_of_squared_distances, 'bx-')
plt.show()

#found yhat for the calculated k value
kmeans = KMeans(n_clusters=3)
model = kmeans.fit(X)
yhat = kmeans.predict(X)

无法弄清楚演员创建散点图。

编辑： 如果质心也是使用绘制的，有没有办法找到最接近质心的演员

centers = kmeans.cluster_centers_（这里的kmeans指的是下面Eric的解决方案）

plt.scatter(centers[:,0],centers[:,1],color='purple',marker='*',label='centroid')

Answer 1

K 表示 Pandas 中的聚类 - 散点图

#!/usr/bin/python3
# -*- coding: utf-8 -*-
import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

df = pd.DataFrame(columns=['Actors', 'Movies', 'TvGuest', "Awards", "Shorts"]) 
df.loc[0]             = ["Robert De Niro",     111,     2,    6,      0] 
df.loc[1]             = ["Jack Nicholson",      70,     2,    4,      0] 
df.loc[2]             = ["Marlon Brando",       64,     4,    5,      0] 
df.loc[3]             = ["Denzel Washington",   25,     2,    3,     24] 
df.loc[4]             = ["Katharine Hepburn",   90,     1,    2,      0] 
df.loc[5]             = ["Humphrey Bogart",     105,    2,    1,      0] 
df.loc[6]             = ["Meryl Streep",        27,     3,    2,      5] 
df.loc[7]             = ["Daniel Day-Lewis",    90,     2,    1,      0] 
df.loc[8]             = ["Sidney Poitier",      63,     2,    3,      0] 
df.loc[9]             = ["Clark Gable",         34,     2,    4,      0] 
df.loc[10]            = ["Ingrid Bergman",      22,     5,    2,      3] 

kmeans = KMeans(n_clusters=4) 
y = kmeans.fit_predict(df[['Movies', 'TvGuest', 'Awards']]) 
df['Cluster'] = y 
plt.scatter(df.Movies, df.TvGuest, c=df.Cluster, alpha = 0.6) 
plt.title('K-means Clustering 2 dimensions and 4 clusters') 
plt.show()

演出：

请注意，二维散点图上显示的数据点是 Movies 和 TvGuest，但 Kmeans 拟合给出了 3 个变量：Movies、TvGuest、Awards。想象一下屏幕上有一个额外的维度，用于计算集群的成员资格。

来源链接：

https://datasciencelab.wordpress.com/2013/12/12/clustering-with-k-means-in-python/

https://datascience.stackexchange.com/questions/48693/perform-k-means-clustering-over-multiple-columns

https://towardsdatascience.com/visualizing-clusters-with-pythons-matplolib-35ae03d87489