python - DBSCAN 用于地理位置数据的聚类

Question

我有一个包含纬度和经度对的数据框。

这是我的数据框的样子。

    order_lat  order_long
0   19.111841   72.910729
1   19.111342   72.908387
2   19.111342   72.908387
3   19.137815   72.914085
4   19.119677   72.905081
5   19.119677   72.905081
6   19.119677   72.905081
7   19.120217   72.907121
8   19.120217   72.907121
9   19.119677   72.905081
10  19.119677   72.905081
11  19.119677   72.905081
12  19.111860   72.911346
13  19.111860   72.911346
14  19.119677   72.905081
15  19.119677   72.905081
16  19.119677   72.905081
17  19.137815   72.914085
18  19.115380   72.909144
19  19.115380   72.909144
20  19.116168   72.909573
21  19.119677   72.905081
22  19.137815   72.914085
23  19.137815   72.914085
24  19.112955   72.910102
25  19.112955   72.910102
26  19.112955   72.910102
27  19.119677   72.905081
28  19.119677   72.905081
29  19.115380   72.909144
30  19.119677   72.905081
31  19.119677   72.905081
32  19.119677   72.905081
33  19.119677   72.905081
34  19.119677   72.905081
35  19.111860   72.911346
36  19.111841   72.910729
37  19.131674   72.918510
38  19.119677   72.905081
39  19.111860   72.911346
40  19.111860   72.911346
41  19.111841   72.910729
42  19.111841   72.910729
43  19.111841   72.910729
44  19.115380   72.909144
45  19.116625   72.909185
46  19.115671   72.908985
47  19.119677   72.905081
48  19.119677   72.905081
49  19.119677   72.905081
50  19.116183   72.909646
51  19.113827   72.893833
52  19.119677   72.905081
53  19.114100   72.894985
54  19.107491   72.901760
55  19.119677   72.905081

我想聚集这些彼此最近的点(200米距离)，下面是我的距离矩阵。

from scipy.spatial.distance import pdist, squareform
distance_matrix = squareform(pdist(X, (lambda u,v: haversine(u,v))))

array([[ 0.        ,  0.2522482 ,  0.2522482 , ...,  1.67313071,
     1.05925366,  1.05420922],
   [ 0.2522482 ,  0.        ,  0.        , ...,  1.44111548,
     0.81742536,  0.98978355],
   [ 0.2522482 ,  0.        ,  0.        , ...,  1.44111548,
     0.81742536,  0.98978355],
   ..., 
   [ 1.67313071,  1.44111548,  1.44111548, ...,  0.        ,
     1.02310118,  1.22871515],
   [ 1.05925366,  0.81742536,  0.81742536, ...,  1.02310118,
     0.        ,  1.39923529],
   [ 1.05420922,  0.98978355,  0.98978355, ...,  1.22871515,
     1.39923529,  0.        ]])

然后我在距离矩阵上应用 DBSCAN 聚类算法。

 from sklearn.cluster import DBSCAN

 db = DBSCAN(eps=2,min_samples=5)
 y_db = db.fit_predict(distance_matrix)

我不知道如何选择 eps 和 min_samples 值。它将太远的点聚集在一个簇中。(距离约2公里)是因为它在聚类时计算欧几里得距离吗？请帮忙。

Answer 1

您可以使用 scikit-learn 的 DBSCAN 对空间经纬度数据进行聚类，而无需预先计算距离矩阵。

db = DBSCAN(eps=2/6371., min_samples=5, algorithm='ball_tree', metric='haversine').fit(np.radians(coordinates))

这来自 clustering spatial data with scikit-learn DBSCAN 上的本教程。特别是，请注意 eps 值仍然是 2km，但它除以 6371 以将其转换为弧度。另外，请注意 .fit() 采用弧度单位的坐标作为半正弦度量。