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python - KMeans聚类后的聚类点(scikit learn)

转载 作者:太空狗 更新时间:2023-10-29 19:32:23 26 4
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我已经使用 sklearn 使用 Kmeans 完成了聚类。虽然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集群的集群点(或者我到目前为止还没有看到它),这很奇怪。有没有一种巧妙的方法来获取每个集群的集群点?

我目前有这个相当笨拙的代码来执行此操作,其中 V 是数据集:

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

非常感谢建议/链接。

谢谢!PD.

最佳答案

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)

你可以得到每个点的簇

estimator.labels_

输出:

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)

然后获取每个簇的点索引

{i: np.where(estimator.labels_ == i)[0] for i in range(estimator.n_clusters)}

输出:

{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])}

编辑

如果您想使用 X 中的点数组而不是索引数组作为值:

{i: X[np.where(estimator.labels_ == i)] for i in range(estimator.n_clusters)}

关于python - KMeans聚类后的聚类点(scikit learn),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/32232067/

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