DBSCAN scikit-learn of Python:将聚类点保存在数组中

Question

以下示例Scikit Learning 的DBSCAN聚类算法示例我试图在数组中存储每个聚类类的x,y

import numpy as np
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler
from pylab import *

# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4, random_state=0)
X = StandardScaler().fit_transform(X) 

xx, yy = zip(*X)
scatter(xx,yy)
show()

db = DBSCAN(eps=0.3, min_samples=10).fit(X)
core_samples = db.core_sample_indices_
labels = db.labels_
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
print n_clusters_
3

我试图通过scikit-learn了解DBSCAN的实现,但从这一点来说,我遇到了麻烦.簇的数量是3(n_clusters_),我希望将每个簇的x,y存储在一个数组中

Answer 1

第一个集群是X[labels == 0],等等:

clusters = [X[labels == i] for i in xrange(n_clusters_)]

而异常值则是

outliers = X[labels == -1]