Name

ST_ClusterKMeans — Windowing function that returns integer id for the cluster each input geometry is in.

Synopsis

integer ST_ClusterKMeans(geometry winset geom, integer number_of_clusters);

Description

Returns 2D distance based k-means cluster number for each input geometry. The distance used for clustering is the distance between the centroids of the geometries.

Availability: 2.3.0

Examples

Generate dummy set of parcels for examples

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('LINESTRING(40 100, 98 100, 100 150, 60 90)'::geometry,
    40, 'endcap=square'),12) As geom;

Original Parcels

Parcels color-coded by cluster number (cid)

SELECT ST_ClusterKMeans(geom, 5) OVER() AS cid, parcel_id, geom
FROM parcels;
-- result
 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...
(7 rows)

 -- Partitioning parcel clusters by type
SELECT ST_ClusterKMeans(geom,3) over (PARTITION BY type) AS cid, parcel_id, type
FROM parcels;
-- result
 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
(7 rows)

See Also

ST_ClusterDBSCAN, ST_ClusterIntersecting, ST_ClusterWithin, ST_Subdivide