描述

退货 K-均值 每个输入几何体的簇号。用于聚集的距离是二维几何图形的质心之间的距离，以及三维几何图形的边界框中心之间的距离。对于点输入，M坐标将被视为输入的权重，并且必须大于0。

max_radius 如果设置，将导致ST_ClusterKMeans生成比 k 确保输出中没有任何群集的半径大于 max_radius 。这在可达性分析中很有用。

增强：3.2.0支持 max_radius

增强：3.1.0对3D几何图形和权重的支持

可用性：2.3.0

示例

生成虚拟地块集，例如：

CREATE TABLE parcels AS
SELECT lpad((row_number() over())::text,3,'0') As parcel_id, geom,
('{residential, commercial}'::text[])[1 + mod(row_number()OVER(),2)] As type
FROM
    ST_Subdivide(ST_Buffer('SRID=3857;LINESTRING(40 100, 98 100, 100 150, 60 90)'::geometry,
    40, 'endcap=square'),12) As geom;

SELECT ST_ClusterKMeans(geom, 3) OVER() AS cid, parcel_id, geom
    FROM parcels;

cid | parcel_id |   geom
-----+-----------+---------------
   0 | 001       | 0103000000...
   0 | 002       | 0103000000...
   1 | 003       | 0103000000...
   0 | 004       | 0103000000...
   1 | 005       | 0103000000...
   2 | 006       | 0103000000...
   2 | 007       | 0103000000...

按类型划分地块簇：

SELECT ST_ClusterKMeans(geom, 3) over (PARTITION BY type) AS cid, parcel_id, type
    FROM parcels;

cid | parcel_id |    type
-----+-----------+-------------
   1 | 005       | commercial
   1 | 003       | commercial
   2 | 007       | commercial
   0 | 001       | commercial
   1 | 004       | residential
   0 | 002       | residential
   2 | 006       | residential

例如：使用3D聚类和加权对预聚聚的行星规模的数据人口数据集进行聚类。根据以下条件确定至少20个地区 孔图尔人口数据 距离其中心不超过3000公里的区域：

create table kontur_population_3000km_clusters as
select
    geom,
    ST_ClusterKMeans(
        ST_Force4D(
            ST_Transform(ST_Force3D(geom), 4978), -- cluster in 3D XYZ CRS
            mvalue := population -- set clustering to be weighed by population
        ),
        20,                      -- aim to generate at least 20 clusters
        max_radius := 3000000    -- but generate more to make each under 3000 km radius
    ) over () as cid
from
    kontur_population;
    

另请参阅

ST_ClusterDBSCAN , ST_ClusterIntersecting , ST_ClusterWithin , ST_Subdivide , ST_Force3D , ST_Force4D ,