---
name: v.cluster
description: Performs cluster identification.
keywords: [ vector, point cloud, cluster, clump, level1 ]
---

# v.cluster

Performs cluster identification.

=== "Command line"

    **v.cluster**
    [**-2bt**]
    **input**=*name*
    **output**=*name*
    [**layer**=*string*]
    [**distance**=*float*]
    [**min**=*integer*]
    [**method**=*string*]
    [**--overwrite**]
    [**--verbose**]
    [**--quiet**]
    [**--qq**]
    [**--ui**]

    Example:

    ```sh
    v.cluster input=name output=name
    ```

=== "Python (grass.tools)"

    *grass.tools.Tools.v_cluster*(**input**,
        **output**,
        **layer**=*"2"*,
        **distance**=*None*,
        **min**=*None*,
        **method**=*"dbscan"*,
        **flags**=*None*,
        **overwrite**=*None*,
        **verbose**=*None*,
        **quiet**=*None*,
        **superquiet**=*None*)

    Example:

    ```python
    tools = Tools()
    tools.v_cluster(input="name", output="name")
    ```

    This grass.tools API is experimental in version 8.5 and expected to be stable in version 8.6.

=== "Python (grass.script)"

    *grass.script.run_command*("***v.cluster***",
        **input**,
        **output**,
        **layer**=*"2"*,
        **distance**=*None*,
        **min**=*None*,
        **method**=*"dbscan"*,
        **flags**=*None*,
        **overwrite**=*None*,
        **verbose**=*None*,
        **quiet**=*None*,
        **superquiet**=*None*)

    Example:

    ```python
    gs.run_command("v.cluster", input="name", output="name")
    ```

## Parameters

=== "Command line"

    **input**=*name* **[required]**  
    &nbsp;&nbsp;&nbsp;&nbsp;Name of input vector map  
    &nbsp;&nbsp;&nbsp;&nbsp;Or data source for direct OGR access  
    **output**=*name* **[required]**  
    &nbsp;&nbsp;&nbsp;&nbsp;Name for output vector map  
    **layer**=*string*  
    &nbsp;&nbsp;&nbsp;&nbsp;Layer number or name for cluster ids  
    &nbsp;&nbsp;&nbsp;&nbsp;Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *2*  
    **distance**=*float*  
    &nbsp;&nbsp;&nbsp;&nbsp;Maximum distance to neighbors  
    **min**=*integer*  
    &nbsp;&nbsp;&nbsp;&nbsp;Minimum number of points to create a cluster  
    **method**=*string*  
    &nbsp;&nbsp;&nbsp;&nbsp;Clustering method  
    &nbsp;&nbsp;&nbsp;&nbsp;Allowed values: *dbscan, dbscan2, density, optics, optics2*  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *dbscan*  
    **-2**  
    &nbsp;&nbsp;&nbsp;&nbsp;Force 2D clustering  
  
    **-b**  
    &nbsp;&nbsp;&nbsp;&nbsp;Do not build topology  
    &nbsp;&nbsp;&nbsp;&nbsp;Advantageous when handling a large number of points  
    **-t**  
    &nbsp;&nbsp;&nbsp;&nbsp;Do not create attribute table  
    **--overwrite**  
    &nbsp;&nbsp;&nbsp;&nbsp;Allow output files to overwrite existing files  
    **--help**  
    &nbsp;&nbsp;&nbsp;&nbsp;Print usage summary  
    **--verbose**  
    &nbsp;&nbsp;&nbsp;&nbsp;Verbose module output  
    **--quiet**  
    &nbsp;&nbsp;&nbsp;&nbsp;Quiet module output  
    **--qq**  
    &nbsp;&nbsp;&nbsp;&nbsp;Very quiet module output  
    **--ui**  
    &nbsp;&nbsp;&nbsp;&nbsp;Force launching GUI dialog

=== "Python (grass.tools)"

    **input** : str, *required*  
    &nbsp;&nbsp;&nbsp;&nbsp;Name of input vector map  
    &nbsp;&nbsp;&nbsp;&nbsp;Or data source for direct OGR access  
    &nbsp;&nbsp;&nbsp;&nbsp;Used as: input, vector, *name*  
    **output** : str, *required*  
    &nbsp;&nbsp;&nbsp;&nbsp;Name for output vector map  
    &nbsp;&nbsp;&nbsp;&nbsp;Used as: output, vector, *name*  
    **layer** : str, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Layer number or name for cluster ids  
    &nbsp;&nbsp;&nbsp;&nbsp;Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.  
    &nbsp;&nbsp;&nbsp;&nbsp;Used as: input, layer  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *2*  
    **distance** : float, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Maximum distance to neighbors  
    **min** : int, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Minimum number of points to create a cluster  
    **method** : str, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Clustering method  
    &nbsp;&nbsp;&nbsp;&nbsp;Allowed values: *dbscan, dbscan2, density, optics, optics2*  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *dbscan*  
    **flags** : str, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Allowed values: *2*, *b*, *t*  
    &nbsp;&nbsp;&nbsp;&nbsp;**2**  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Force 2D clustering  
  
    &nbsp;&nbsp;&nbsp;&nbsp;**b**  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Do not build topology  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Advantageous when handling a large number of points  
    &nbsp;&nbsp;&nbsp;&nbsp;**t**  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Do not create attribute table  
    **overwrite** : bool, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Allow output files to overwrite existing files  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *None*  
    **verbose** : bool, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Verbose module output  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *None*  
    **quiet** : bool, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Quiet module output  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *None*  
    **superquiet** : bool, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Very quiet module output  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *None*  

    Returns:

    **result** : grass.tools.support.ToolResult | None  
    If the tool produces text as standard output, a *ToolResult* object will be returned. Otherwise, `None` will be returned.

    Raises:

    *grass.tools.ToolError*: When the tool ended with an error.

=== "Python (grass.script)"

    **input** : str, *required*  
    &nbsp;&nbsp;&nbsp;&nbsp;Name of input vector map  
    &nbsp;&nbsp;&nbsp;&nbsp;Or data source for direct OGR access  
    &nbsp;&nbsp;&nbsp;&nbsp;Used as: input, vector, *name*  
    **output** : str, *required*  
    &nbsp;&nbsp;&nbsp;&nbsp;Name for output vector map  
    &nbsp;&nbsp;&nbsp;&nbsp;Used as: output, vector, *name*  
    **layer** : str, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Layer number or name for cluster ids  
    &nbsp;&nbsp;&nbsp;&nbsp;Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.  
    &nbsp;&nbsp;&nbsp;&nbsp;Used as: input, layer  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *2*  
    **distance** : float, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Maximum distance to neighbors  
    **min** : int, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Minimum number of points to create a cluster  
    **method** : str, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Clustering method  
    &nbsp;&nbsp;&nbsp;&nbsp;Allowed values: *dbscan, dbscan2, density, optics, optics2*  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *dbscan*  
    **flags** : str, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Allowed values: *2*, *b*, *t*  
    &nbsp;&nbsp;&nbsp;&nbsp;**2**  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Force 2D clustering  
  
    &nbsp;&nbsp;&nbsp;&nbsp;**b**  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Do not build topology  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Advantageous when handling a large number of points  
    &nbsp;&nbsp;&nbsp;&nbsp;**t**  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Do not create attribute table  
    **overwrite** : bool, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Allow output files to overwrite existing files  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *None*  
    **verbose** : bool, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Verbose module output  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *None*  
    **quiet** : bool, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Quiet module output  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *None*  
    **superquiet** : bool, *optional*  
    &nbsp;&nbsp;&nbsp;&nbsp;Very quiet module output  
    &nbsp;&nbsp;&nbsp;&nbsp;Default: *None*

## DESCRIPTION

*v.cluster* partitions a point cloud into clusters or clumps.

If the minimum number of points is not specified with the **min**
option, the minimum number of points to constitute a cluster is *number
of dimensions + 1*, i.e. 3 for 2D points and 4 for 3D points.

If the maximum distance is not specified with the **distance** option,
the maximum distance is estimated from the observed distances to the
neighbors using the upper 99% confidence interval.

*v.cluster* supports different methods for clustering. The recommended
methods are **method=dbscan** if all clusters should have a density
(maximum distance between points) not larger than **distance** or
**method=density** if clusters should be created separately for each
observed density (distance to the farthest neighbor).

### Clustering methods

#### dbscan method

The [Density-Based Spatial Clustering of Applications with
Noise](https://en.wikipedia.org/wiki/DBSCAN) is a commonly used
clustering algorithm. A new cluster is started for a point with at least
*min* - 1 neighbors within the maximum distance. These neighbors are
added to the cluster. The cluster is then expanded as long as at least
*min* - 1 neighbors are within the maximum distance for each point
already in the cluster.

#### dbscan2 method

Similar to *dbscan*, but here it is sufficient if the resultant cluster
consists of at least **min** points, even if no point in the cluster has
at least *min - 1* neighbors within **distance**.

#### density method

This method creates clusters according to their point density. The
maximum distance is not used. Instead, the points are sorted ascending
by the distance to their farthest neighbor (core distance), inspecting
*min - 1* neighbors. The densest cluster is created first, using as
threshold the core distance of the seed point. The cluster is expanded
as for DBSCAN, with the difference that each cluster has its own maximum
distance. This method can identify clusters with different densities and
can create nested clusters.

#### optics method

This method is [Ordering Points to Identify the Clustering
Structure](https://en.wikipedia.org/wiki/OPTICS_algorithm). It is
controlled by the number of neighbor points (option *min* - 1). The core
distance of a point is the distance to the farthest neighbor. The
reachability of a point *q* is its distance from a point *p* (original
optics: max(core-distance(p), distance(p, q))). The aim of the *optics*
method is to reduce the reachability of each point. Each unprocessed
point is the seed for a new cluster. Its neighbors are added to a queue
sorted by smallest reachability if their reachability can be reduced.
The points in the queue are processed and their unprocessed neighbors
are added to a queue sorted by smallest reachability if their
reachability can be reduced.

The *optics* method does not create clusters itself, but produces an
ordered list of the points together with their reachability. The output
list is ordered according to the order of processing: the first point
processed is the first in the list, the last point processed is the last
in the list. Clusters can be extracted from this list by identifying
valleys in the points' reachability, e.g. by using a threshold value. If
a maximum distance is specified, this is used to identify clusters,
otherwise each separated network will constitute a cluster.

The OPTICS algorithm uses each yet unprocessed point to start a new
cluster. The order of the input points is arbitrary and can thus
influence the resultant clusters.

#### optics2 method

**EXPERIMENTAL** This method is similar to OPTICS, minimizing the
reachability of each point. Points are reconnected if their reachability
can be reduced. Contrary to OPTICS, a cluster's seed is not fixed but
changed if possible. Each point is connected to another point until the
core of the cluster (seed point) is reached. Effectively, the initial
seed is updated in the process. Thus separated networks of points are
created, with each network representing a cluster. The maximum distance
is not used.

## NOTES

By default, cluster IDs are stored as category values of the points in
layer 2.

## EXAMPLE

Analysis of random points for areas in areas of the vector *urbanarea*
(North Carolina sample dataset).

First generate 1000 random points within the areas the vector urbanarea
and within the subregion, then do clustering and visualize the result:

```sh
# pick a subregion of the vector urbanarea
g.region -p n=272950 s=188330 w=574720 e=703090 res=10

# create random points in areas
v.random output=random_points npoints=1000 restrict=urbanarea

# identify clusters
v.cluster input=random_points output=clusters_optics method=optics

# set random vector color table for the clusters
v.colors map=clusters_optics layer=2 use=cat color=random

# display in command line
d.mon wx0

# note the second layer and transparent (none) color of the circle border
d.vect map=clusters_optics layer=2 icon=basic/point size=10 color=none
```

![Four different methods with default settings applied to 1000
random points](v_cluster_4_methods.png)  
*Figure: Four different methods with default settings applied to 1000
random points generated in the same way as in the example.*

Generate random points for analysis (100 points per area), use different
method for clustering and visualize using color stored the attribute
table.

```sh
# pick a subregion of the vector urbanarea
g.region -p n=272950 s=188330 w=574720 e=703090 res=10

# create clustered points
v.random output=rand_clust npoints=100 restrict=urbanarea -a

# identify clusters
v.cluster in=rand_clust out=rand_clusters method=dbscan

# create colors for clusters
v.db.addtable map=rand_clusters layer=2 columns="cat integer,grassrgb varchar(11)"
v.colors map=rand_clusters layer=2 use=cat color=random rgb_column=grassrgb

# display with your preferred method
# remember to use the second layer and RGB column
# for example use
d.vect map=rand_clusters layer=2 color=none rgb_column=grassrgb icon=basic/circle
```

## SEE ALSO

*[r.clump](r.clump.md), [v.hull](v.hull.md),
[v.distance](v.distance.md)*

## AUTHOR

Markus Metz

## SOURCE CODE

Available at: [v.cluster source code](https://github.com/OSGeo/grass/tree/main/vector/v.cluster)
([history](https://github.com/OSGeo/grass/commits/main/vector/v.cluster))  
Latest change: Wednesday May 07 13:01:38 2025 in commit [f20dbc6](https://github.com/OSGeo/grass/commit/f20dbc611e2caa7b8ef368c71d9f0452fe5940eb)
