Dynamic Clustering - How many matches do you look for?

From what I understand of dynamic clustering, and the way I am currently looking at implementing mine, you store a history of all stats and which angles you would have hit the target at. Then when choosing your targeting angle you select the top N closest matches to the targets current state, and then select the angle to fire from those top N. My question is, does anyone have a good ballpark figure for the value of N?

If N is too small you might not have enough data about the target to get accuracy. If N is too large you might end up including too much information, polluting your pool with bad matches.

Or, do you not take N all the time, but instead only take matches which satisfy criteria on how good the match is, i.e only matches which are 5% different to the targets current state?

Anyone have opinions on this?

P.s If this is the wrong place to discuss, tell me and I will move it to the correct place! :)

Its worth noting that only taking the matches to within 5% might not produce enough matches and will have the same problem as N too small. So you could combine it - select matches to 5%, if not enough, select the top N best of the rest. If you have more than N matches to 5%, then take all of those 5%. Thoughts?

Of course then we would need to start discussing both N and match accuracy % values! ;)

I take the top sqrt(tree.size()) scans, limited at 100. I think it's a pretty good balance between 'generality' and 'specificity'.