Description

DBSCAN seems not to use multiple processors (n_jobs argument ignored)
it looks like dbscan hands the arguments off to nearest neighbor, but NN only uses the n_jobs arguments for certain clustering types (presumably not ones that dbscan calls by default). It would be good to mention how to change input to use the n_jobs parameter, and possibly modify the default values to make it useful.

Steps/Code to Reproduce

code taken from:
http://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py

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

centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=1000000, centers=centers, cluster_std=0.4,
random_state=0)

X = StandardScaler().fit_transform(X)

db = DBSCAN(eps=0.3, min_samples=10, n_jobs=-1).fit(X)

Expected Results

answer is correct but the job should be split between processors, and time consumed should be significantly less.

Actual Results

seems to run on only one processor

Versions

import platform; print(platform.platform())
Linux-3.13.0-101-generic-x86_64-with-Ubuntu-14.04-trusty
import sys; print("Python", sys.version)
Python 3.4.3 (default, Nov 17 2016, 01:08:31)
[GCC 4.8.4]
import numpy; print("NumPy", numpy.version)
NumPy 1.11.2
import scipy; print("SciPy", scipy.version)
SciPy 0.18.1
import sklearn; print("Scikit-Learn", sklearn.version)
Scikit-Learn 0.18.1