labels= db.labels_# Number ofclusters in labels, ignoring noise if present.n_clusters_=len(set(labels)) - (1if-1in labels else0)n_noise_=list(labels).count(-1)print('Estimatednumber of clusters: %d'% n_clusters_)print('Estimated numberof noise points: %d'% n_noise_)print("Homogene...
labels = db.labels_# Number of clusters in labels, ignoring noise if present. n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0) n_noise_ = list(labels).count(-1)print('Estimated number of clusters: %d' % n_clusters_) print('Estimated number of noise points: %d' %...
5. 簇间平均距离(Between-Cluster Sum of Squares,BSS):用于衡量聚类之间的距离,值越小表示聚类效果越好。 6. 簇数(Number of Clusters,N):合适的簇数是评价聚类结果好坏的一个重要指标。 7. 噪声点(Noise):评价聚类算法对噪声点的处理能力,噪声点的数量越少,聚类效果越好。 8. 计算时间(Runtime):评价聚类...
labels = db.labels_# Number of clusters in labels, ignoring noise if present. n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0) n_noise_ = list(labels).count(-1)print('Estimated number of clusters: %d' % n_clusters_) print('Estimated number of noise points: %d' %...
plt.title('Estimated number of clusters: %d' % n_clusters_) plt.show() 如果不进行第二步中的扩展,所有的小圆点都应该是噪声点(不符合第一步核心点的要求) 5)算法优缺点 a)优点 可以发现任意形状的聚类 b)缺点 随着数据量的增加,对I/O、内存的要求也随之增加。
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() 输出 代码语言:javascript 代码运行次数:0 复制 Cloud Studio代码运行 估计的聚类个数为:3同质性:0.953完整性:0.883V-measure:0.917调整...
print('Estimated number of clusters: %d'%n_clusters_) print("Silhouette Coefficient: %0.3f"%metrics.silhouette_score(X,labels)) #打印各簇标号以及各簇内数据 foriinrange(n_clusters_): print('Cluster ',i,':') print(list(X[labels==i].flatten())) ...
[db.core_sample_indices_] =True22labels =db.labels_2324#Number of clusters in labels, ignoring noise if present.25n_clusters_ = len(set(labels)) - (1if-1inlabelselse0)2627print('Estimated number of clusters: %d'%n_clusters_)28print("Homogeneity: %0.3f"%metrics.homogeneity_score(labels...
(db.labels_,dtype=bool)core_samples_mask[db.core_sample_indices_]=Truelabels=db.labels_# 聚类结果的噪声数据点标记为-1n_clusters_=len(set(labels))-(1if-1inlabelselse0)n_noise_=list(labels).count(-1)print('Estimated number of clusters: %d'%n_clusters_)print('Estimated number of noise...
xy=X[class_member_mask & ~core_samples_mask]# 将所有属于该类的非核心样本取出,使用小图标绘制 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()...