如何实现 K-means (Python)？

Question

我有一个数据集，包含 61 行和 28 列（已使用应用程序的类别）。我对编码非常陌生，我将第一次尝试机器学习算法，所以我很感激任何帮助！ .

这就是我的数据：

Category  Calendar  Clock  Communication  Education  Entertainment  Finance   
UserId                                                                         
  1                1      1              1          9              1         0   
  2                0      1              1          0              0         0   
  3                0      1              1          0              0         0   
  4                1      1              6          0              0         0   
  5                0      1              1          1              1         0   
  6                0      0              0          0              0         0   
  7                0      1              1          0              0         0   
  8                0      1              1          0              0         0   
  9                0      0              1          0              7         0   
  10               0      1              1          0              0         0   
  11               0      2              1          0              5         0   
  13               0      0              1          0              0         0   
  14               0      1              1          0              1         0   
  15               1      1              1          0              0         0   
  16               0      1              1          0              0         0   
  17               0      0              1          1              1         0   
  19               0      1              1          0              0         0   
  20               0      0              1          0              0         0   
  21               0      1              1          0              1         0   
  .....  
  61               0      1              1          0              5         0   
                 

我正在寻找一个 k-means 的实现，以将用户分为 3 个组，并根据他们使用的 category 的行为。 我该怎么办！

Answer 1

我想这就是你要找的。​​p>

from pylab import plot,show
from numpy import vstack,array
from numpy.random import rand
import numpy as np
from scipy.cluster.vq import kmeans,vq
import pandas as pd
import pandas_datareader as dr
from math import sqrt
from sklearn.cluster import KMeans
from matplotlib import pyplot as plt
data = np.asarray([np.asarray(df['Feature1']),np.asarray(df['Feature2']),[np.asarray(df['Feature3'])]).T
X = data
distorsions = []
for k in range(2, 20):
    k_means = KMeans(n_clusters=k)
    k_means.fit(X)
    distorsions.append(k_means.inertia_)
fig = plt.figure(figsize=(15, 5))
plt.plot(range(2, 20), distorsions)
plt.grid(True)
plt.title('Elbow curve')

centroids,_ = kmeans(data,3)
# assign each sample to a cluster
idx,_ = vq(data,centroids)
# some plotting using numpy's logical indexing
plot(data[idx==0,0],data[idx==0,1],'ob',
     data[idx==1,0],data[idx==1,1],'oy',
     data[idx==2,0],data[idx==2,1])
plot(centroids[:,0],centroids[:,1],'sg',markersize=8)
show()

details = [(name,cluster) for name, cluster in zip(df.index,idx)]
for detail in details:
    print(detail)