v.cluster  Performs cluster identification.
vector, point cloud, cluster, clump, level1
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]
 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
 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
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).
The DensityBased 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.
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.
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.
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(coredistance(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.
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.
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
r.clump, v.hull, v.distance
Markus Metz
Last changed: $Date: 20150907 10:09:13 +0200 (Mon, 07 Sep 2015) $
Available at: v.cluster source code (history)
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