Most clustering algorithms assume that data points in each row are independent. I have some data with repeated measurements from individuals.
I can use a standard algorithm, and then check to see if samples from the same person end up in the same cluster (for example by manual inspection of a dendrogram, or by looking at within group homogeneity and stability measures using
clValid's "biological" validation).
Are there any clustering algorithms (preferably with an implementation in R) that take account of the repeated measurements while calculating clusters?
My dataset is very wide (more variables than samples), so being able to deal with that situation would be very useful.
Also, there are different numbers of measurements for individuals, so it would also be nice for the algorithms to deal with that.
The variables in my dataset are continuous rather than categorical.