DBSCAN in scikit-learn of Python: save the cluster points in an array

Question

following the example Demo of DBSCAN clustering algorithm of Scikit Learning i am trying to store in an array the x, y of each clustering class

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

I'm trying to understand the DBSCAN implementation by scikit-learn, but from this point I'm having trouble. The number of cluster is 3 (n_clusters_) and I wish to store the x, y of each cluster in an array

Answer 1

The first cluster is X[labels == 0], etc.:

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

and the outliers are

outliers = X[labels == -1]

Answer 2

What do you mean by "of each cluster"?

In DBSCAN, clusters are not represented as centroids as in k-means, so there is no obvious representation of the cluster except its members. You already have the x and y position of the cluster members, as they are the input data.

So I'm not sure what the question is.