Efficient way of finding overlapping elements of sets and dispersing those elements b/w the sets

Hi, I need some help with a problem we are trying to solve: We need an
efficient way of finding the counts of different sets taking into
account the overlap between the different sets. Also the counts of the
different sets
should take into account the way the overlaps occur between the sets.
i.e. the counts to disperse the elements of the overlap between the
two sets needs to take into account the universe count of each set and
then use a ratio of the universe counts to disperse the overlapping
elements.

To better illustrate this, lets say there are two sets, whose
universes are depicted by A and B respectively:

Scenario I (Two Sets A & B):
-----------------------------------

Count (A) = 2000 // Universe Count for Set A

Count (B) = 3000 // Universe Count for Set B

Count (A & B) = 100 // Count for overlapping
elements b/w Sets A & B

Count (A - B) = 1900 // Count for elements
belonging uniquely to Set A (and not B)

Count (B - B) = 2900 // Count for elements
belonging uniquely to Set B (and not A)


Now the new counts for sets A & B will be computed as follows:

Real_Count_A = Count(A - B) +
Count(A & B) * [Count(A) / (Count(A) + Count(B))]

Real_Count_B = Count(B - A) +
Count(A & B) * [Count(B) / (Count(A) + Count(B))]

i.e.

Real_Count_A = 1900 +
100 * [2000 / (2000 + 3000)]

Real_Count_A = 1900 + 40 = 1940


Real_Count_B = 2900 +
100 * [2000 / (2000 + 3000)]

Real_Count_B = 2900 + 60 = 2960


So the count for Set A is made up of 1940 elements of which 1900
elements belong exclusively to it plus the 40% of the 100 overlapping
elements.
Similarly the count for Set B is made up of 2960 elements of which
2900 elements belong exclusively to it plus the 60% of the 100
overlapping elements.
[ This is because the ratio to disperse the overlapping
elements b/w A and B was Count A / Count B = 2000 / 3000 = 40% / 60% ]


Now if you were to extend this scenario to 3 Sets, the logic becomes:

Scenario II (Three Sets A, B & C):
-----------------------------------

Real_Count_A = Count(A - B - C)
+ Count(A & B & ~C) * [Count(A) / (Count(A) + Count(B))]
+ Count(A & ~B & C) * [Count(A) / (Count(A) + Count(C))]
+ Count(A & B & C) * [Count(A) / (Count(A) + Count(B) + Count(C))]

Real_Count_B = Count(B - A - C)
+ Count(B & A & ~C) * [Count(B) / (Count(A) + Count(B))]
+ Count(B & ~A & C) * [Count(B) / (Count(B) + Count(C))]
+ Count(A & B & C) * [Count(B) / (Count(A) + Count(B) + Count(C))]

Real_Count_C = Count(C - A - B)
+ Count(C & B & ~A) * [Count(C) / (Count(B) + Count(C))]
+ Count(C & ~B & A) * [Count(C) / (Count(A) + Count(C))]
+ Count(A & B & C) * [Count(C) / (Count(A) + Count(B) + Count(C))]


Scenario N: (N Sets)
---------------------

If we generalize this for "N" Sets, I think there will be N * 2^(N-1)
Total Terms to compute the counts for the "N" Sets.


--------------------------------------------------------------------
# of Sets = N Total Terms (N X 2 N-1 )
--------------------------------------------------------------------
1 1
2 4
3 12
4 32
5 80
10 5120
15 491520
20 10485760
--------------------------------------------------------------------

If we use the above formula (using mathematical induction), there will
be an
exponential rise in the number of terms to be computed as the number
of sets increases.

What I would like to know is if there is any efficient method of
finding the same counts (at least approximate counts) for the sets by
using a more simpler formulae or other heuristic methods / tools?

Any pointers will be greatly appreciated, Thanks!
Leo