The clm mate manual
Keywords: manual

16 May 2014 clm mate 14-137

1.
NAME
2.
SYNOPSIS
3.
DESCRIPTION
4.
OPTIONS
5.
AUTHOR
6.
SEE ALSO

## NAME

clm mate — compute best matches between two clusterings

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

## SYNOPSIS

clm mate [-o fname (output file name)] [-b (omit headers)] [--one-to-many (require multiple hits in <clfile1>)] [-h (print synopsis, exit)] [--apropos (print synopsis, exit)] [--version (print version, exit)] <clfile1> <clfile2>

## DESCRIPTION

clm mate computes for each cluster `X` in `clfile1` all clusters `Y` in `clfile2` that have non-empty intersection and outputs a line with the data points listed below.

overlap(X,Y) # 2 * size(meet(X,Y)) / (size(X)+size(Y)) index(X) # name of cluster index(Y) # name of cluster size(meet(X,Y)) size(X-Y) # size of left difference size(Y-X) # size of right difference size(X) size(Y) projection(X, clfile2) # see below projection(Y, clfile1) # see below

The projected size of a cluster `X` relative to a clustering `K` is simply the sum of all the nodes shared between any cluster `Y` in `K` and `X`, duplications allowed. For example, the projected size of `(0,1)` relative to `{(0,2,4), (1,4,9), (1,3,5)}` equals `3`.

The overlap between `X` and `Y` is exactly 1.0 if the two clusters are identical, and for nearly identical clusterings the score will be close to 1.0.

All of this information can also be obtained from the contingency matrix defined for two clusterings. The `[i,j]` row-column entry in a contigency matrix between to clusterings gives the number of entries in the intersection between cluster `i` and cluster `j` from the respective clusterings. The other information is implicitly present; the total number of nodes in clusters `i` and `j` for example can be obtained as the sum of entries in row `i` and column `j` respectively, and the difference counts can then be obtained by substracting the intersection count. The contingency matrix can easily be computed using mcx; e.g.

mcx /clfile2 lm /clfile1 lm tp mul /ting wm

will create the contingency matrix in mcl matrix format in the file `ting`, where columns range over the clusters in `clfile1`.

The output can be put to good use by sorting it numerically on that first score field. It is advisable to use a stable sort routine (use the -s option for UNIX sort) From this information one can quickly extract the closest clusters between two clusterings.

## OPTIONS

-o fname (output file name)

Specify the name of the output file.

-b (omit headers)

Batch mode, omit column names.

--one-to-many (require multiple hits in <clfile1>)

Do not output information for clusters in the first file that are subset of a cluster in the second file.

## AUTHOR

Stijn van Dongen.

## SEE ALSO

mclfamily for an overview of all the documentation and the utilities in the mcl family.