Clustering data for occurrence I have a set of data representing nodes and how often they have been involved with each other. I've processed this into a table containing the nodes on X and Y with the data being the number of interactions e.g.
   A    B    C    D
A  2    2    0    0
B  2    2    0    0
C  0    0    2    1
D  0    0    1    2

So the data can be looked at either way. I would now like to perform some clustering of these to find clusters of related nodes through their level of interaction, as well as some visualisation. I have R which is ideal but am happy to use something else if that's easier.
All the for-noddy clustering I see doesn't have this type of data: it would say have a set of nodes and for each a set of variables and clustering is for similarity in those variables - a different problem than I'm attempting.
 A: From what you say it sounds like you want to use igraph and find groups of individuals using moduarity, you can look here for details.
e.g.
dat <-
structure(list(ID = structure(1:4, .Label = c("A", "B", "C", 
"D"), class = "factor"), A = c(2L, 2L, 0L, 0L), B = c(2L, 2L, 
0L, 0L), C = c(0L, 0L, 2L, 1L), D = c(0L, 0L, 1L, 2L)), .Names = c("ID", 
"A", "B", "C", "D"), class = "data.frame", row.names = c(NA, 
-4L))

#> dat
#  ID A B C D
#1  A 2 2 0 0
#2  B 2 2 0 0
#3  C 0 0 2 1
#4  D 0 0 1 2

library(igraph)
library(reshape2)
g <- melt(dat)
g <- g[g$ID != g$variable ,]
names(g) <- c("from", "to", "weight")

g <- graph.data.frame(g, vertices = unique(g[1]))

wtc <- walktrap.community(g)
modularity(wtc)
#[1] 0.4444444

wtc
#Graph community structure calculated with the walktrap algorithm
#Number of communities (best split): 2 
#Modularity (best split): 0.4444444 
#Membership vector:
#B C D A 
#2 1 1 2 


modularity(g, membership(wtc))
#[1] -0.1666667

This also has visualisation and you can easily colour things by community membership :)
plot(g, vertex.color = wtc$membership)


