clmmate(1)

NAME
clmmate - compute best matches between two clusterings

SYNOPSIS
clmmate [-twins fname] <clfile1> <clfile2>

DESCRIPTION
clmmate computes for each cluster C1 in clfile1 all clusters C2 in clfile2 that have non-empty intersection, and prints the number of shared nodes, the numbers of nodes in C1 absent in C2, the number of nodes in C2 absent in C1, and the total number of nodes in C2. The total number of nodes in C1 is listed once for each sublisting, where the primary listing is over clusters in clfile1. 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. clmmate has the advantage that information is presented in a more comprehensible fashion than available from the contingency matrix. Additionally, the -twins twinfile option is useful to inspect the list of best matching clusters from clfile2. This option will create a file twinfile which contains cryptic (but useful) lines of the form
0.645           0,2         194    214      0
0.004           0,3           1    407    128
0.211           0,38         48    360      0
0.203           0,40         46    362      0
0.154           0,56         34    374      0
0.145           0,63         32    376      0
The first line indicates that clusters 0 from clfile1 and 2 from clfile2 have 194 nodes in their intersection; there are 214 additional nodes in cluster 0, and 0 additional nodes in cluster 2. Denote the size of the intersection (also called meet) by m, and the sizes of the two clusters as s1 and s2 respectively. The scores beginning each line are computed as 2*m/(s1+s2). Such a score equals exactly 1.0 if the two clusters are identical, and for nearly identical clusterings the score will be close to 1.0. The twin file 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) An example result is the following.
1.000           4,1         253      0      0
1.000          17,7         119      0      0
1.000          20,10         94      0      0
1.000          26,17         71      0      0
1.000          28,18         69      0      0
1.000          30,20         66      0      0
1.000          38,27         55      0      0
1.000          41,29         53      0      0
1.000          51,41         44      0      0
1.000          64,51         35      0      0
1.000          69,62         32      0      0
1.000          71,61         32      0      0
1.000          74,70         30      0      0
1.000          76,73         28      0      0
1.000          80,76         27      0      0
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.