为什么 kmeans 每次都给出完全相同的结果？

Question

我重新运行 kmeans 4次并得到

根据其他答案，我明白了

Everytime K-Means initializes the centroid, it is generated randomly.

能否请您解释一下为什么每次的结果都完全一样？

import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt 
from sklearn.cluster import KMeans
%config InlineBackend.figure_format = 'svg' # Change the image format to svg for better quality
don = pd.read_csv('https://raw.githubusercontent.com/leanhdung1994/Deep-Learning/main/donclassif.txt.gz', sep=';')

fig, ax = plt.subplots(nrows=2, ncols=2, figsize= 2 * np.array(plt.rcParams['figure.figsize']))

for row in ax:
    for col in row:
        kmeans = KMeans(n_clusters = 4)
        kmeans.fit(don)
        y_kmeans = kmeans.predict(don)
        col.scatter(don['V1'], don['V2'], c = y_kmeans, cmap = 'viridis')
        centers = kmeans.cluster_centers_
        col.scatter(centers[:, 0], centers[:, 1], c = 'red', s = 200, alpha = 0.5);

plt.show()

Answer 1

我 post @AEF 的评论将此问题从未回答列表中删除。

Random initialziation does not necessarily mean random result. Easiest example: k-means with k=1 always finds the mean in one step, regardless of where the center is initialised.

Answer 2

它们不一样。他们很相似。 K-means 是一种迭代移动质心的算法，以便它们在拆分数据时变得越来越好，虽然这个过程是确定性的，但您必须为这些质心选择初始值，这通常是随机完成的。随机开始，并不意味着最终的质心是随机的。他们会收敛到比较好的东西，而且往往是相似的。

通过这个简单的修改查看您的代码：

import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt 
from sklearn.cluster import KMeans
%config InlineBackend.figure_format = 'svg' # Change the image format to svg for better quality
don = pd.read_csv('https://raw.githubusercontent.com/leanhdung1994/Deep-Learning/main/donclassif.txt.gz', sep=';')

fig, ax = plt.subplots(nrows=2, ncols=2, figsize= 2 * np.array(plt.rcParams['figure.figsize']))

cc = []

for row in ax:
    for col in row:
        kmeans = KMeans(n_clusters = 4)
        kmeans.fit(don)
        cc.append(kmeans.cluster_centers_)
        y_kmeans = kmeans.predict(don)
        col.scatter(don['V1'], don['V2'], c = y_kmeans, cmap = 'viridis')
        centers = kmeans.cluster_centers_
        col.scatter(centers[:, 0], centers[:, 1], c = 'red', s = 200, alpha = 0.5);

plt.show()

cc

如果您查看这些质心的确切值，它们将如下所示：

[array([[ 4.97975722,  4.93316461],
        [ 5.21715504, -0.18757547],
        [ 0.31141141,  0.06726803],
        [ 0.00747797,  5.00534801]]),
 array([[ 5.21374245, -0.18608103],
        [ 0.00747797,  5.00534801],
        [ 0.30592308,  0.06549162],
        [ 4.97975722,  4.93316461]]),
 array([[ 0.30066361,  0.06804847],
        [ 4.97975722,  4.93316461],
        [ 5.21017831, -0.18735444],
        [ 0.00747797,  5.00534801]]),
 array([[ 5.21374245, -0.18608103],
        [ 4.97975722,  4.93316461],
        [ 0.00747797,  5.00534801],
        [ 0.30592308,  0.06549162]])]

相似但不同的值集。

还有：

查看 KMeans 的默认参数。有一个叫做 n_init:

Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.

默认情况下它等于 10。这意味着每次您运行 k-means 它实际上运行 10 次并选择最好的结果。这些最佳结果将比 k-means 中单个运行的结果更加相似。

为什么 kmeans 每次都给出完全相同的结果？

Why does kmeans give exactly the same results everytime?

k-means

python-3.x

scikit-learn