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CentOS 7.0 - man page for cluster (centos section 1)

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CLUSTER(1)									       CLUSTER(1)

NAME
       cluster - find clusters in a graph and augment the graph with this information.

SYNOPSIS
       cluster [-v?]  [ -Ck ] [ -ck ] [ -o outfile ] [ files ]

DESCRIPTION
       cluster	takes  as input a graph in DOT format, finds node clusters and augments the graph
       with this information.  The clusters are specified by the "cluster" attribute attached  to
       nodes;  cluster	values are non-negative integers.  cluster attempts to maximize the modu-
       larity of the clustering.  If the edge attribute "weight" is defined, this will be used in
       computing the clustering.

OPTIONS
       The following options are supported:

       -Ck    specifies  a  targeted  number of clusters that should be generated.  The specified
	      number k is only a suggestion and may not be realisable.	If k == 0,  the  default,
	      the number of clusters that approximately optimizes the modularity is returned.

       -ck    specifies  clustering method.  If k == 0, the default, the modularity will be used.
	      If k == 1 modularity quality will be used.

       -ooutfile
	      Specifies that output should go into the file outfile. By default, stdout is used.

       -v     Verbose mode.

EXAMPLES
       Applying cluster to the following graph,

	  graph {
	      1--2 [weight=10.]
	      2--3 [weight=1]
	      3--4 [weight=10.]
	      4--5 [weight=10]
	      5--6 [weight=10]
	      3--6 [weight=0.1]
	      4--6 [weight=10.]
	     }

       gives

	  graph {
		node [cluster="-1"];
		1 [cluster=1];
		2 [cluster=1];
		3 [cluster=2];
		4 [cluster=2];
		5 [cluster=2];
		6 [cluster=2];
		1 -- 2 [weight="10."];
		2 -- 3 [weight=1];
		3 -- 4 [weight="10."];
		4 -- 5 [weight=10];
		5 -- 6 [weight=10];
		3 -- 6 [weight="0.1"];
		4 -- 6 [weight="10."];
	  }

AUTHOR
       Yifan Hu <yifanhu@research.att.com>

SEE ALSO
       gvmap(1)

       Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities
       in large networks. Journal of Statistical Mechanics: Theory and Experiment (2008), P10008.

					   3 March 2011 			       CLUSTER(1)
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