# Demo of DBSCAN clustering algorithm¶

Finds core samples of high density and expands clusters from them.

import numpy as np

from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler


## 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)


## Compute DBSCAN¶

db = DBSCAN(eps=0.3, min_samples=10).fit(X)
labels = db.labels_

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)

print("Estimated number of clusters: %d" % n_clusters_)
print("Estimated number of noise points: %d" % n_noise_)
print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
print(
)
print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, labels))


Out:

Estimated number of clusters: 3
Estimated number of noise points: 18
Homogeneity: 0.953
Completeness: 0.883
V-measure: 0.917
Silhouette Coefficient: 0.626


## Plot result¶

import matplotlib.pyplot as plt

# Black removed and is used for noise instead.
unique_labels = set(labels)
colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]
for k, col in zip(unique_labels, colors):
if k == -1:
# Black used for noise.
col = [0, 0, 0, 1]

plt.plot(
xy[:, 0],
xy[:, 1],
"o",
markerfacecolor=tuple(col),
markeredgecolor="k",
markersize=14,
)

plt.plot(
xy[:, 0],
xy[:, 1],
"o",
markerfacecolor=tuple(col),
markeredgecolor="k",
markersize=6,
)

plt.title("Estimated number of clusters: %d" % n_clusters_)
plt.show() Total running time of the script: ( 0 minutes 0.089 seconds)

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