kmeans集群:如何访问集群数据点

Question

以下是我从kmeans-scikit文档和一篇讨论kmeans:的博客文章中收集的kmeans算法的实现

#http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
#http://fromdatawithlove.thegovans.us/2013/05/clustering-using-scikit-learn.html
from sklearn.cluster import KMeans
import numpy as np
from matplotlib import pyplot
X = np.array([[10, 2 , 9], [1, 4 , 3], [1, 0 , 3],
[4, 2 , 1], [4, 4 , 7], [4, 0 , 5], [4, 6 , 3],[4, 1 , 7],[5, 2 , 3],[6, 3 , 3],[7, 4 , 13]])
kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
k = 3
kmeans.fit(X)
labels = kmeans.labels_
centroids = kmeans.cluster_centers_
for i in range(k):
# select only data observations with cluster label == i
ds = X[np.where(labels==i)]
# plot the data observations
pyplot.plot(ds[:,0],ds[:,1],'o')
# plot the centroids
lines = pyplot.plot(centroids[i,0],centroids[i,1],'kx')
# make the centroid x's bigger
pyplot.setp(lines,ms=15.0)
pyplot.setp(lines,mew=2.0)
pyplot.show()
print(kmeans.cluster_centers_.squeeze())

如何打印/访问k个集群中每个集群的数据点。

if k = 3 : 
cluster 1 : [10, 2 , 9], [1, 4 , 3], [1, 0 , 3]                  
cluster 2 : [4, 0 , 5], [4, 6 , 3],[4, 1 , 7],[5, 2 , 3],[6, 3 , 3],[7, 4 , 13]
cluster 3 : [4, 2 , 1], [4, 4 , 7]

阅读http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.htmlkmeans对象上没有用于此的属性或方法？

更新：

kmeans.labels_返回array([1, 0, 2, 0, 2, 2, 0, 2, 0, 0, 1], dtype=int32)

但是，这是如何显示3个集群中每个集群中的数据点的呢？

Answer 1

如果您使用适合的KMeans对象的_labels属性，您将获得每个训练向量的集群分配的数组。标签数组的顺序与训练数据相同，因此您可以对它们进行压缩，或者为每个唯一标签执行numpy.where()操作。

Answer 2

访问k-means聚类后的数据点：

添加代码：

sortedR = sorted(result, key=lambda x: x[1])
sortedR

完整代码：

from sklearn.cluster import KMeans
import numpy as np
from matplotlib import pyplot
X = np.array([[10, 2 , 9], [1, 4 , 3], [1, 0 , 3],
[4, 2 , 1], [4, 4 , 7], [4, 0 , 5], [4, 6 , 3],[4, 1 , 7],[5, 2 , 3],[6, 3 , 3],[7, 4 , 13]])
kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
k = 3
kmeans = KMeans(n_clusters=k)
kmeans.fit(X)
labels = kmeans.labels_
centroids = kmeans.cluster_centers_
for i in range(k):
# select only data observations with cluster label == i
ds = X[np.where(labels==i)]
# plot the data observations
pyplot.plot(ds[:,0],ds[:,1],'o')
# plot the centroids
lines = pyplot.plot(centroids[i,0],centroids[i,1],'kx')
# make the centroid x's bigger
pyplot.setp(lines,ms=15.0)
pyplot.setp(lines,mew=2.0)
pyplot.show()
result = zip(X , kmeans.labels_)
sortedR = sorted(result, key=lambda x: x[1])
sortedR