Ejemplo de código de centros de clúster DBSCAN

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Ejemplo 1: algoritmo de agrupación en clústeres dbscan

print(__doc__)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)
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))-(1if-1in labels else0)
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("Adjusted Rand Index: %0.3f"% metrics.adjusted_rand_score(labels_true, labels))print("Adjusted Mutual Information: %0.3f"% metrics.adjusted_mutual_info_score(labels_true, labels))print("Silhouette Coefficient: %0.3f"% metrics.silhouette_score(X, labels))# ############################################################################## Plot resultimport 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 inzip(unique_labels, colors):if k ==-1:# Black used for noise.
        col =[0,0,0,1]

    class_member_mask =(labels == k)

    xy = X[class_member_mask & core_samples_mask]
    plt.plot(xy[:,0], xy[:,1],'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=14)

    xy = X[class_member_mask &~core_samples_mask]
    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()

Ejemplo 2: algoritmo de agrupación en clústeres dbscan

Density-Based Spatial Clustering of Applications with Noise (DBSCAN)is an unsupervised clustering algorithm which is based on the idea of
clustering the points forming contiguous regions of high points density.

These clusters are separated from other such clusters which are also
contguous regions of high points density.

It overcomes the problem of clustering even the
loosely related observations of K Means.

It also produces better results as compared to K Means
for a variety of different distributions.

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