## table of contents

v.cluster(1grass) | GRASS GIS User's Manual | v.cluster(1grass) |

# NAME¶

**v.cluster** - Performs cluster identification.

# KEYWORDS¶

vector, point cloud, cluster, clump, level1

# SYNOPSIS¶

**v.cluster**

**v.cluster --help**

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

## Flags:¶

**-2**-

Force 2D clustering **-b**-

Do not build topology

Advantageous when handling a large number of points **-t**-

Do not create attribute table **--overwrite**-

Allow output files to overwrite existing files **--help**-

Print usage summary **--verbose**-

Verbose module output **--quiet**-

Quiet module output **--ui**-

Force launching GUI dialog

## Parameters:¶

**input**=*name***[required]**-

Name of input vector map

Or data source for direct OGR access **output**=*name***[required]**-

Name for output vector map **layer**=*string*-

Layer number or name for cluster ids

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.

Default:*2* **distance**=*float*-

Maximum distance to neighbors **min**=*integer*-

Minimum number of points to create a cluster **method**=*string*-

Clustering method

Options:*dbscan, dbscan2, density, optics, optics2*

Default:*dbscan*

# 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).

## dbscan¶

The Density-Based Spatial Clustering of Applications with Noise 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¶

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¶

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¶

This method is Ordering Points to Identify the Clustering
Structure. 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¶

**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:

# 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

*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.

# 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,* *v.hull,* *v.distance*

# AUTHOR¶

Markus Metz

# SOURCE CODE¶

Available at: v.cluster source code (history)

Accessed: Saturday Jul 27 17:08:40 2024

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