KMeans聚类后的聚类点(scikit学习) [英] cluster points after KMeans clustering (scikit learn)
问题描述
我已经使用sklearn使用Kmeans进行了聚类.尽管它有一种打印质心的方法,但我发现scikit-learn没有一种方法可以打印出每个聚类的聚类点(或者到目前为止我还没有看到),这真是令人惊讶.是否有一种巧妙的方法来获取每个群集的群集点?
I have done clustering using Kmeans using sklearn. While it has a method to print the centroids, I am finding it rather bizzare that scikit-learn doesn't have a method to print out the cluster-points of each cluster (or that I have not seen it so far). Is there a neat way to get the cluster-points of each cluster?
我目前有一个相当笨拙的代码来做,其中V是数据集:
I currently have this rather cludgy code to do it, where V is the dataset:
def getClusterPoints(V, labels):
clusters = {}
for l in range(0, max(labels)+1):
data_points = []
indices = [i for i, x in enumerate(labels) if x == l]
for idx in indices:
data_points.append(V[idx])
clusters[l] = data_points
return clusters
建议/链接非常感谢.
谢谢! PD.
推荐答案
例如
import numpy as np
from sklearn.cluster import KMeans
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target
estimator = KMeans(n_clusters=3)
estimator.fit(X)
您可以通过以下方式获得每个点的聚类
You can get clusters of each point by
estimator.labels_
出局:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 2, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1,
2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,
1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1], dtype=int32)
然后获取每个聚类的点的索引
Then get the indices of points for each cluster
{i: np.where(estimator.labels_ == i)[0] for i in range(estimator.n_clusters)}
出局:
{0: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),
1: array([ 50, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
91, 92, 93, 94, 95, 96, 97, 98, 99, 101, 106, 113, 114,
119, 121, 123, 126, 127, 133, 138, 142, 146, 149]),
2: array([ 52, 77, 100, 102, 103, 104, 105, 107, 108, 109, 110, 111, 112,
115, 116, 117, 118, 120, 122, 124, 125, 128, 129, 130, 131, 132,
134, 135, 136, 137, 139, 140, 141, 143, 144, 145, 147, 148])}
修改
如果要将X
中的点数组用作值而不是索引数组:
If you want to use array of points in X
as values rather than the array of indices:
{i: X[np.where(estimator.labels_ == i)] for i in range(estimator.n_clusters)}
这篇关于KMeans聚类后的聚类点(scikit学习)的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!