I've used the Mfuzz in R for clustering time-course microarray data sets. Mfuzz uses "soft-clustering". Basically, individuals can appear in more than one group. Here's some example output to give you an idea of what it does:

I've used the Mfuzz in R for clustering time-course microarray data sets. Mfuzz uses "soft-clustering". Basically, individuals can appear in more than one group.

As @Andy points out in the comment, the original paper uses CTN data. However, I suspect that it should work OK for your discrete data. Especially since you are just exploring the data set. Here's a quick example in R:

##It's a bioconductor package
library(Mfuzz)
library(Biobase)

## Simulate some data
## 6 time points and 90 individuals
tps = 6;cases = 90
d = rpois(tps*cases, 1)  ##Poisson distribution with mean 1
m = matrix(d, ncol=tps, nrow=cases)

##First 30 individuals have increasing trends
m[1:30,] = t(apply(m[1:30,], 1, cumsum))

##Next 30 have decreasing trends
##A bit hacky, sorry
m[31:60,] = t(apply(t(apply(m[31:60,], 1, cumsum)), 1, rev))

##Last 30 individuals have random numbers from a Po(1)

##Create an expressionSet object
tmp_expr = new('ExpressionSet', exprs=m)

##Specify c=3 clusters
cl = mfuzz(tmp_expr, c=3, m=1.25)
mfuzz.plot(tmp_expr,cl=cl, mfrow=c(2, 2))

Gives the following plot: