I have 100 groups and each group has some elements inside. For the cross validation, I want to make five bins which their size is as equal as possible.
Is there any algo
This seems related to the set partitioning problem, which is NP-hard but fortunately admits lots of good approximation algorithms and pseudopolynomial-time dynamic programming algorithms. You may want to look into those as a starting point, since there's already quite a lot of work that's been done in this area.
Hope this helps!
If you're looking for a clustering algorithm (partitioning method) with equal size constraint, I would suggest the Spectral Clustering. It will satisfy your demand for clusters with almost the same sizes because it solves the normalized cut problem, which try to find a balanced cut.
This is not a cluster analysis problem (I rewrote the question to use the more appropriate wording for you). Cluster analysis is a structure discovery task.
Instead, have a look at the following two related problems from computer science:
All of these appear to be NP-hard, so you will want to use an approximation only (if you have large data, with just 5 examples you can easily brute-force all combinations)