We will then populate the centroid table by generating the cluster ID for each geometry in chp03.earthquakes using the ST_ClusterKMeans function, and then we will use the ST_Centroid function to calculate the 10 centroids for each cluster:

INSERT INTO chp04.earthq_cent (the_geom, cid) ( 
  SELECT DISTINCT ST_SetSRID(ST_Centroid(tab2.ge2), 4326) as centroid,
  tab2.cid FROM( 
    SELECT ST_UNION(tab.ge) OVER (partition by tab.cid ORDER BY tab.cid) 
    as ge2, tab.cid as cid FROM( 
      SELECT ST_ClusterKMeans(e.the_geom, 10) OVER() AS cid, e.the_geom 
      as ge FROM chp03.earthquakes as e) as tab 
  )as tab2 
); 

If we check the inserted rows with the following command:

SELECT * FROM chp04.earthq_cent; 

The output will be as follows:

Then, insert the corresponding minimum bounding circles for the clusters in the chp04.earthq_circ table. Execute the following SQL command:

# INSERT INTO chp04.earthq_circ (the_geom, cid) ( 
  SELECT DISTINCT ST_SetSRID( 
    ST_MinimumBoundingCircle(tab2.ge2), 4326) as circle, tab2.cid 
    FROM( 
      SELECT ST_UNION(tab.ge) OVER (partition by tab.cid ORDER BY tab.cid) 
      as ge2, tab.cid as cid 
      FROM( 
        SELECT ST_ClusterKMeans(e.the_geom, 10) OVER() as cid, e.the_geom 
        as ge FROM chp03.earthquakes AS e 
      ) as tab 
    )as tab2 
  ); 

In a desktop GIS, import all three tables as layers (chp03.earthquakes, chp04.earthq_cent, and chp04.earthq_circ) in order to visualize them and understand the clustering. Note that circles may overlap; however, this does not mean that clusters do as well, since each point belongs to one and only one cluster, but the minimum bounding circle for a cluster may overlap with another minimum bounding circle for another cluster: