


  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 num-
      bers, 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  mea-
        sures for the result.

      -tf <tf-spec> (transform input matrix values)



  clm info 1.008, 09-308      4 Nov 2009                          1





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


        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 cherryp-
        ick 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)

  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  Nether-
      lands, Amsterdam, May 2000.
      http://www.cwi.nl/ftp/CWIreports/INS/INS-R0012.ps.Z





















  clm info 1.008, 09-308      4 Nov 2009                          2


