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)

  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 1.008, 09-308            4 Nov 2009                       clm info(1)
