> For the complete documentation index, see [llms.txt](https://docs.carto.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.carto.com/data-and-analysis/analytics-toolbox-for-redshift/sql-reference/clustering.md).

# clustering

This module contains functions that perform clustering on geographies.

## CREATE\_CLUSTERKMEANS <a href="#create_clusterkmeans" id="create_clusterkmeans"></a>

```sql
CREATE_CLUSTERKMEANS(input, output_table, geom_column, number_of_clusters)
```

**Description**

Takes a set of points as input and partitions them into clusters using the k-means algorithm. Creates a new table with the same columns as `input` plus a `cluster_id` column with the cluster index for each of the input features.

**Input parameters**

* `input`: `VARCHAR` name of the table or literal SQL query to be clustered.
* `output_table`: `VARCHAR(MAX)` qualified name of the output table, e.g. `<my-schema>.<my-output-table>`. The process will fail if the table already exists.
* `geom_column`: `VARCHAR` name of the column to be clusterd.
* `number_of_clusters`: `INT` number of clusters that will be generated.

{% hint style="warning" %}
**warning**

Keep in mid that due to some restrictions in the Redshift `VARCHAR` size, the maximum number of features (points) allow to be clustered is around 2500.
{% endhint %}

**Examples**

{% code overflow="wrap" lineNumbers="true" %}

```sql
CALL carto.CREATE_CLUSTERKMEANS('<my-schema>.<my-table>', '<my-schema>.<my-output-table>', 'geom', 5);
-- The table `<my-schema>.<my-output-table>` will be created
-- adding the column cluster_id to those in `<my-schema>.<my-table>`.
```

{% endcode %}

{% code overflow="wrap" lineNumbers="true" %}

```sql
CALL carto.CREATE_CLUSTERKMEANS('SELECT * FROM <my-schema>.<my-table>', '<my-schema>.<my-output-table>', 'geom', 5);
-- The table `<my-schema>.<my-output-table>` will be created
-- adding the column cluster_id to those returned in the input query.
```

{% endcode %}

## ST\_CLUSTERKMEANS <a href="#st_clusterkmeans" id="st_clusterkmeans"></a>

```sql
ST_CLUSTERKMEANS(geog [, numberOfClusters])
```

**Description**

Takes a set of points as input and partitions them into clusters using the k-means algorithm. Returns an array of tuples with the cluster index for each of the input features and the input geometry.

**Input parameters**

* `geog`: `GEOMETRY` points to be clustered.
* `numberOfClusters` (optional): `INT` number of clusters that will be generated. It defaults to the square root of half the number of points (`sqrt(<NUMBER OF POINTS>/2)`). The output number of cluster cannot be greater to the number of distinct points of the `geog`.

**Return type**

`SUPER`: containing objects with `cluster` as the cluster id and `geom` as the geometry in GeoJSON format.

**Examples**

{% code overflow="wrap" lineNumbers="true" %}

```sql
SELECT carto.ST_CLUSTERKMEANS(ST_GEOMFROMTEXT('MULTIPOINT ((0 0), (0 1), (5 0), (1 0))'));
-- {"cluster":0,"geom":{"type":"Point","coordinates":[0.0,0.0]}}
-- {"cluster":0,"geom":{"type":"Point","coordinates":[0.0,1.0]}}
-- {"cluster":0,"geom":{"type":"Point","coordinates":[5.0,0.0]}}
-- {"cluster":0,"geom":{"type":"Point","coordinates":[1.0,0.0]}}
```

{% endcode %}

{% code overflow="wrap" lineNumbers="true" %}

```sql
SELECT carto.ST_CLUSTERKMEANS(ST_GEOMFROMTEXT('MULTIPOINT ((0 0), (0 1), (5 0), (1 0))'), 2);
-- {"cluster":0,"geom":{"type":"Point","coordinates":[0.0,0.0]}}
-- {"cluster":0,"geom":{"type":"Point","coordinates":[0.0,1.0]}}
-- {"cluster":1,"geom":{"type":"Point","coordinates":[5.0,0.0]}}
-- {"cluster":0,"geom":{"type":"Point","coordinates":[1.0,0.0]}}
```

{% endcode %}


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