Given two arrays, how can one determine the the intersection?

I have two datasets (A & B). They each have 1000 numbers.

99% of the time: A < x <= B

However, 1% of the time B < x < A.

How can I solve for x, where x has the highest probability of separating the two groups.

Obviously Max(A) and Min(B) are misleading because there are occasional anomalies (<2%). Can you help me determine the optimum "x" with the highest probability on both sides?

Sample Dataset

A 1
A 1
A 1
A 2
B 2 <--anomoly
A 3
A 3
A 3
A 4
A 5 <--anomoly
B 5 <--division, or x
B 5
B 5
B 5
A 6 <--anomoly
B 7
B 8
B 8
B 8
B 9
B 9
B 10
B 10

• It seems you want to minimize the number of $B$s below $x$ plus the number of $A$s above $x$? – Hagen von Eitzen Aug 26 '13 at 16:50
• Or perhaps, you want to minimize the maximum of the number of $B$s below $x$ and the number of $A$s above $x$? – dtldarek Aug 26 '13 at 17:09
• Correct. What do you recommend? – Ryan Aug 26 '13 at 17:10
• Looks like your dataset is sorted. Why not take the boundary as the point which the smallest 99% of A is less, i.e. 990 instances of A is less than x? – peterwhy Aug 26 '13 at 17:14
• Or given whatever metric you want to minimize, you can do a single pass through your sorted data (2000 points) and minimize over all possible cutoffs (2000 potential locations). You can condense your data and just use the frequency histogram, too. – Evan Aug 26 '13 at 17:38

Now, we have a sequence of 1000 A's and 1000 B's mixed together. Initialise an error counter $e=1000$.
Sequentially scan the sequence. If an A is encountered, decrease $e$ by 1. If a B is encountered instead, increase $e$ by 1.
Also, store the split point that minimises $e$ throughout the scan. The corresponding number value, which equals to the average of the number values before and after the split, will be the boundary that minimises number of mis-classification.