[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

採用基於區域的自動種子區域生長法的彩色圖像分割方法

 

From: Brian Kent: Density Based Clustering in Python

 

 

聚類演示：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:

 

 

補充，一個效果一樣好的算法：Level Set Tree 

 

加載方式：

import debacl as dcl