الگوریتم DBSCAN

				
					import matplotlib.pyplot as plt 

import numpy as np 

from sklearn.cluster import DBSCAN 

from sklearn import metrics 

from sklearn.datasets import make_blobs 

from sklearn.preprocessing import StandardScaler 

from sklearn import datasets 

# Load data in X 

X, y_true = make_blobs(n_samples=300, centers=4, 

cluster_std=0.50, random_state=0) 

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

core_samples_mask = np.zeros_like(db.labels_, dtype=bool)                                 

core_samples_mask[db.core_sample_indices_] = True 

labels = db.labels_ 

# Number of clusters in labels, ignoring noise if present. 

n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0) 

  

# Plot result 

# Black removed and is used for noise instead. 

unique_labels = set(labels) 

colors = ['y', 'b', 'g', 'r'] 

print(colors) 

for k, col in zip(unique_labels, colors): 

if k == -1: 

# Black used for noise. 

col = 'k' 

class_member_mask = (labels == k) 

xy = X[class_member_mask & core_samples_mask] 

plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col, 

markeredgecolor='k', 

markersize=6) 

xy = X[class_member_mask & ~core_samples_mask] 

plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col, 

markeredgecolor='k', 

markersize=6) 

plt.title('number of clusters: %d' % n_clusters_) 

plt.show()