sklearn DBSCAN

DBSCAN 该聚类算法利用点周围密度的概念进行聚类，不一定要求类具有凸性， 因为其可通过密度构造出噪声点。 参数min_sample、eps给出核心点选择的定义，与esp为半径最少包含min_samples的为 核心点。 -1标记噪声点，非负整数标记类别。 numpy.zeros_like: 生成与给定序列shape相同的序列。 下面是一个例子：（大点为核心点，小点为边界点，黑点为噪声点） 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 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) 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_ 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) import matplotlib.pyplot as plt unique_labels = set(labels) colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels))) for k, col in zip(unique_labels, colors):  if k == -1:   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 = 14)  xy = X[class_member_mask & -core_samples_mask]  plt.plot(xy[:,0], xy[:,1], 'o', markerfacecolor = col,     markeredgecolor = 'k', markersize = 6) plt.title("Estimated number of clusters: %d" % n_clusters_) plt.show()