[Scikit-learn] *2.3 Clustering - DBSCAN: Density-Based Spatial Clustering of Applications with Noise

http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN

采用基于区域的自动种子区域生长法的彩色图像分割方法

From: Brian Kent: Density Based Clustering in Python

聚类演示：https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/

print(__doc__)

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

# #############################################################################

# 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)) - (1 if -1 in labels else 0)

print('Estimated number of clusters: %d' % n_clusters_)

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 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]

    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()

Result:

补充，一个效果同样好的算法：Level Set Tree

加载方式：

import debacl as dcl