I have a challenge laid out before me. I need to divide the incoming Oxford
student class into 25 groups of about 16 or 17 students each. However, they
want the groups to be as balanced as possible, across number, sex, race, and
geographic origin. Now, I can easily see how to balance based on
Damn good question. Here's what I would do...
For each classification, find out how many groups there are and how
many belong to each group. For gender, you have 2 groups and they are
probably split 50/50. So every addition to each bucket of students
would be male, then female, etc.
Dean's method is one possibility. This is actually a very interesting
question and I'm nojt sure how I'd solve it. I thought about it a bit
during my drive home, and here's the approach I would take... This is
alot easier if there are only two choices for each statistic
(male|female -
Or, you normalize with eigenvectors. Just determine the McClaurean
equivalent, factor the Jacobian and viola!
Actually, in a take on Dean's suggestion you could try a weighting
function. Simply assign a numeric value for each classification,
bucketize the results by sum of the numeric values for
Interesting task!
Unless there's some whitepaper out there explaining the real right answer,
Cameron's mention of how to test the effectiveness is great.
One way that occurred to me is have a meta table with a column for
attribute-name and a column with the value of importance (would be
