如何在 DBSCAN sklearn 中获取质心？

Question

我正在使用 DBSCAN 进行聚类。但是，现在我想从代表它的每个簇中选择一个点，但我意识到 DBSCAN 没有像 kmeans 中那样的质心。

但是，我发现 DBSCAN 有一个叫做 core points 的东西。我在想是否可以使用这些核心点或任何其他替代方法从每个集群中获得一个代表点。

我在下面提到了我使用过的代码。

import numpy as np
from math import pi
from sklearn.cluster import DBSCAN

#points containing time value in minutes
points = [100, 200, 600, 659, 700]

def convert_to_radian(x):
    return((x / (24 * 60)) * 2 * pi)

rad_function = np.vectorize(convert_to_radian)
points_rad = rad_function(points)

#generate distance matrix from each point
dist = points_rad[None,:] - points_rad[:, None]

#Assign shortest distances from each point
dist[((dist > pi) & (dist <= (2*pi)))] = dist[((dist > pi) & (dist <= (2*pi)))] -(2*pi)
dist[((dist > (-2*pi)) & (dist <= (-1*pi)))] = dist[((dist > (-2*pi)) & (dist <= (-1*pi)))] + (2*pi) 
dist = abs(dist)

#check dist
print(dist)

#using default values, set metric to 'precomputed'
db = DBSCAN(eps=((100 / (24*60)) * 2 * pi ), min_samples = 2, metric='precomputed')

#check db
print(db)

db.fit(dist)

#get labels
labels = db.labels_

#get number of clusters
no_clusters = len(set(labels)) - (1 if -1 in labels else 0)

print('No of clusters:', no_clusters)
print('Cluster 0 : ', np.nonzero(labels == 0)[0])
print('Cluster 1 : ', np.nonzero(labels == 1)[0])

print(db.core_sample_indices_)

如果需要，我很乐意提供更多详细信息。

Answer 1

你为什么不估计结果估计簇的质心？

points_of_cluster_0 = dist[labels==0,:]
centroid_of_cluster_0 = np.mean(points_of_cluster_0, axis=0) 
print(centroid_of_cluster_0)

points_of_cluster_1 = dist[labels==1,:]
centroid_of_cluster_1 = np.mean(points_of_cluster_1, axis=0)
print(centroid_of_cluster_1)

如何在 DBSCAN sklearn 中获取质心？

How to get the centorids in DBSCAN sklearn?

python

cluster-analysis

dbscan

scikit-learn