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       v.cluster  - Performs cluster identification.


       vector, point cloud, cluster, clump, level1


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

           Force 2D clustering

           Do not build topology
           Advantageous when handling a large number of points

           Do not create attribute table

           Allow output files to overwrite existing files

           Print usage summary

           Verbose module output

           Quiet module output

           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 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

           Maximum distance to neighbors

           Minimum number of points to create a cluster

           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

       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 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.

       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

       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(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.

       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: 2015-09-07 10:09:13 +0200 (Mon, 07 Sep 2015) $


       Available at: v.cluster source code (history)

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