clm info(1)							  USER COMMANDS 						       clm info(1)

  NAME
      clm info - compute performance measures for graphs and clusterings.

      clminfo  is not in actual fact a program. This manual page documents the behaviour and options of the clm program when invoked in mode info.
      The options -h, --apropos, --version, -set, --nop are accessible in all clm modes. They are described in the clm manual page.

  SYNOPSIS
      clm info [options] <graph file> <cluster file> <cluster file>*

      clm info [-o fname (write to file fname)] [-pi f (apply inflation beforehand)] [-tf spec (apply tf-spec to input	matrix)]  [-cl-tree  fname
      (expect  file with nested clusterings)] [-cl-ceil <num> (skip clusters of size exceeding <num>)] [-cat-max num (do at most num tree levels)]
      [--node-self-measures (dump measure for native cluster)] [--node-all-measures (dump measure for  incident  cluster)]  [-h  (print  synopsis,
      exit)] [--apropos (print synopsis, exit)] [--version (print version, exit)] <matrix file> <cluster file> <cluster file>*

  DESCRIPTION
      clm  info  computes several numbers indicative for the efficiency with with a clustering captures the edge mass of a given graph.  Use it in
      conjunction with clm dist to determine which clusterings you accept. See the EXAMPLES section in clm dist for an example of clm dist and clm
      info (and clm meet) usage.  Output can be generated for multiple clusterings at the same time.

      The  efficiency  factor  is  described in [1] (see the REFERENCES section). It tries to balance the dual aims of capturing a lot of edges or
      edge weights and keeping the cluster footprint or area fraction small. The efficiency number has several appealing mathematical  properties,
      cf. [1]. It is related to, but not derivable from, the second and third numbers, the mass fraction and the area fraction.

      The mass fraction is defined as follows.	Let e be an edge of the graph. The clustering captures e if the two nodes associated with e are in
      the same cluster.  Now the mass fraction is the joint weight of all captured edges divided by the joint weight of all  edges  in	the  input
      graph.

      The  area  fraction  is roughly the sum of the squares of all cluster sizes for all clusters in the clustering, divided by the square of the
      number of nodes in the graph. It says roughly, because the actual formula uses the quantity N*(N-1) wherever it says square (of N) above.  A
      low/high area fraction indicates a fine-grained/coarse clustering.

  OPTIONS
      -o fname (output file name)

      -pi f (apply inflation beforehand)
	Apply inflation to the graph matrix and compute the performance measures for the result.

      -tf <tf-spec> (transform input matrix values)
	shared_defopt{-tf}

      -cl-tree fname (expect file with nested clusterings (cone format))
      -cl-ceil <num> (skip (nested) clusters of size exceeding <num>)
	The  specified	file  should contain a hierarchy of nested clusterings such as generated by mclcm. The output is then in a special format,
	undocumented but easy to understand.  Its purpose is to help cherrypick a single clustering from a tree, in conjunction with the  slightly
	experimental and undocumented program mlmfifofum.

	The  measure that is used is very slow to compute for large clusters, and generally it will be outside any interesting range (i.e. it will
	be small).  Use -cl-ceil to skip clusters exceeding the specified size - clm info will directly proceed to subclusters if they exist.

      -cat-max num (do at most num levels)
	This only has effect when used with -cl-tree.  clm info will start at the most fine-grained level, working upwards.

      --node-all-measures (dump node-wise criteria for all incident clusters)
      --node-self-measures (dump node-wise criteria for native cluster)
	These options return a key-value based format, with the meaning of the keys as follows.

	nm    file name (redundant unless multiple cluster files are provided)
	ni    node index
	ci    cluster index
	nn    number of neighbours of this node (constant for a give node)
	nc    cluster size (constant for a given cluster)
	ef    efficiency for this node/cluster combination
	em    max-efficiency for this node/cluster combination
	mf    mass fraction: percentage of edge weights for this node in this cluster
	ma    total mass of edge weights for this node in this cluster
	xn    number of neighbours of the node that are not in the cluster
	xc    number of nodes in the cluster that are not a neighbour of the node
	ns    number of neighbours of the node that are also in this cluster
	ti    the maximum of the edge weights for neighbours of this node that are in this cluster
	to    the maximum of the edge weights for neighbours of this node that are NOT in this cluster
	al    (alien) 1 if the node is not native to the cluster, 0 if the node is native

  AUTHOR
      Stijn van Dongen.

  SEE ALSO
      mclfamily(7) for an overview of all the documentation and the utilities in the mcl family.

  REFERENCES
      [1] Stijn van Dongen. Performance criteria for graph clustering  and  Markov  cluster  experiments.  Technical  Report  INS-R0012,  National
      Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam, May 2000.
      http://www.cwi.nl/ftp/CWIreports/INS/INS-R0012.ps.Z

  clm info 12-068						      8 Mar 2012							 clm info(1)