Python中使用DBSCAN算法对坐标点进行聚类例子

超、凢脫俗 2022-05-25 10:10 620阅读 0赞

在服务器安装了Anaconda4，Anaconda附带了一大批常用数据科学包，方便Python进行数据分析。

#!/usr/bin/python
    # -*- coding: UTF-8
    
    ''' DBSCAN算法测试程序 @author: 程序兔 @date: 2018-05-08 '''
    
    from math import *
    from sklearn.cluster import DBSCAN
    
    
    def get_distance(array_1, array_2):
        lon_a = array_1[0]
        lat_a = array_1[1]
        lon_b = array_2[0]
        lat_b = array_2[1]
        radlat1 = radians(lat_a)
        radlat2 = radians(lat_b)
        a = radlat1 - radlat2
        b = radians(lon_a) - radians(lon_b)
        s = 2 * asin(sqrt(pow(sin(a/2),2) + cos(radlat1) * cos(radlat2)*pow(sin(b/2),2)))
        earth_radius = 6378137
        s = s * earth_radius
        return s
    
    
    def create_coordinate_list():
        result = []
        # 第1个聚类簇，3个点在500米范围内
        point_11 = [116.501146, 39.9915]
        point_12 = [116.501452,39.991002]
        point_13 = [116.501685,39.990491]
    
        # 第2个聚类簇，2个点在500米范围内
        point_21 = [116.528509,39.995549]
        point_22 = [116.530808,39.99389]
    
        # 噪音点
        point_31 = [116.419131,40.024949]
    
        # 第3个聚类簇，2个点在500米范围内
        point_41 = [116.51913,39.972384]
        point_42 = [116.520747,39.972025]
    
        # 噪音点
        point_51 = [116.53075,39.883273]
    
        result.append(point_11)
        result.append(point_12)
    
    
        result.append(point_21)
        result.append(point_22)
    
        result.append(point_31)
    
        result.append(point_41)
        result.append(point_42)
    
        # 此处加入第1个聚类簇的第3个点
        result.append(point_13)
    
        result.append(point_51)
    
        return result
    
    
    def main():
        # 模拟坐标数据
        coordinate_list = create_coordinate_list()
    
        # DBSCAN聚类
        dbscan = DBSCAN(eps=500, min_samples=2, metric=get_distance).fit(coordinate_list)
    
        # 输出[ 0 0 1 1 -1 2 2 0 -1]
        print(dbscan.labels_)
    
    
    if __name__ == '__main__':
        main()

dbscan.labels\_数组就是聚类簇索引列表，噪音点为-1，其他代表各个簇的索引，顺序依次对应坐标点列表。