Ensembles in clustering are not that well researched yet.
In supervised learning, you can use your target knowledge to optimize ensembles. However, how do you do unsupervised optimization?
There are some approaches on trying to maximize diversity while also looking for consensus. But the problem here is that a lot of the methods are strict, and their results are not really comparable. So a consensus may either be something too obvious or even an indication that both methods failed, at which point you didn't gain anything, actually.
The problem is here, that clustering is not just (unsupervised) learning. Actually it is a lot of "unsupervised" and not really learning. You need to look at it from a "knowledge discovery" (tell me something new about my data) view, not a "machine learning" viewpoint (label new data just like we already did with the previous data).
It's really a different mind set.
Sorry, there is no "best" solution, because IMHO there is no "good" solution yet.
There is quite some research going on though. I have not been following all the MultiClust workshops, but these are highly relevant for what you are looking for.