DBSCAN用于集群地理位置数据 [英] DBSCAN for clustering of geographic location data

问题描述

我有一个包含经度和纬度对的数据框。

I have a dataframe with latitude and longitude pairs.

这是我的数据框。

    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

我想将此点彼此最靠近的点聚类（200米rs距离）以下是我的距离矩阵。

I want to cluster this points which are nearest to each other(200 meters distance) following is my distance matrix.

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。

Then I am applying DBSCAN clustering algorithm on distance matrix.

 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& min_samples值。它将太远的点聚集在一个群集中。（距离约2 km）是因为它在群集时计算欧几里德距离吗？

I don't know how to choose eps & min_samples value. It clusters the points which are way too far, in one cluster.(approx 2 km in distance) Is it because it calculates euclidean distance while clustering? please help.

推荐答案

DBSCAN是 meant ，可用于原始数据，并具有空间索引加速。我所知道的唯一可以加速地理距离的工具是 ELKI （Java）-scikit-learn仅支持一些距离，例如欧几里得距离（请参见 sklearn.neighbors.NearestNeighbors ）。
但是，显然，您可以尝试预先计算两两之间的距离，所以这还不是问题。

DBSCAN is meant to be used on the raw data, with a spatial index for acceleration. The only tool I know with acceleration for geo distances is ELKI (Java) - scikit-learn unfortunately only supports this for a few distances like Euclidean distance (see sklearn.neighbors.NearestNeighbors). But apparently, you can affort to precompute pairwise distances, so this is not (yet) an issue.

但是，您没有足够仔细地阅读文档，并且您认为DBSCAN使用距离矩阵是错误的：

However, you did not read the documentation carefully enough, and your assumption that DBSCAN uses a distance matrix is wrong:

from sklearn.cluster import DBSCAN
db = DBSCAN(eps=2,min_samples=5)
db.fit_predict(distance_matrix)

使用欧几里得距离距离矩阵行，这显然没有任何意义。

uses Euclidean distance on the distance matrix rows, which obviously does not make any sense.

请参见 DBSCAN 的文档（添加了重点） ：

See the documentation of DBSCAN (emphasis added):

class sklearn.cluster.DBSCAN（eps = 0.5，min_samples = 5， metric ='euclidean'，algorithm =' auto'，leaf_size = 30，p =无，random_state =无）

class sklearn.cluster.DBSCAN(eps=0.5, min_samples=5, metric='euclidean', algorithm='auto', leaf_size=30, p=None, random_state=None)

指标：字符串或可调用

计算要素阵列中实例之间的距离时使用的度量。如果metric是字符串或可调用，则它必须是metric.pairwise.calculate_distance为其metric参数所允许的选项之一。 如果度量是预先计算的，则X被假定为距离矩阵，并且必须是正方形。 X可以是稀疏矩阵，在这种情况下，仅非零元素可以被视为DBSCAN的邻居。 / p>

The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by metrics.pairwise.calculate_distance for its metric parameter. If metric is "precomputed", X is assumed to be a distance matrix and must be square. X may be a sparse matrix, in which case only "nonzero" elements may be considered neighbors for DBSCAN.

与 fit_predict 类似：

X ：形状（n_samples，n_features）的形状的数组或稀疏（CSR）矩阵，或形状（n_samples，n_samples）的形状的数组（稀疏）

X : array or sparse (CSR) matrix of shape (n_samples, n_features), or array of shape (n_samples, n_samples)

特征数组，或样本之间的距离数组 if metric ='precomputed'。

A feature array, or array of distances between samples if metric='precomputed'.

换句话说，您需要做

db = DBSCAN(eps=2, min_samples=5, metric="precomputed")

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