The clmmate manual

27 Feb 2006 clmmate 1.006, 06-058

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

## NAME

clmmate - compute best matches between two clusterings

## SYNOPSIS

clmmate [-l] [-o fname] <clfile1> <clfile2>

## DESCRIPTION

clmmate 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

Use the -l option to include a legend heading the output.

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.

## AUTHOR

Stijn van Dongen.

## SEE ALSO

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