Provided by:
mcl_06-021-1_i386 
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, dupli‐
cations 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 iden‐
tical, 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 inter‐
section 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(7) for an overview of all the documentation and the utilities
in the mcl family.
clmmate 1.006, 06-021 21 Jan 2006 clmmate(1)