Well there is normalization , regularization, and standardization, but it basically depends on what you want to do and what implications that has for your data.

X is the set and x is a value in that set.

So:

1 - { [ max(X) - x ] / [ max(X)- min(X) ] }

or alternatively,

[x- min(X)] / [max(X)-min(X)]

Should do what you are asking, which sounds like normalization. That will normalize your values between 0 and 1. However, it wont do anything about your data being skewed to one side or the other. So the mean of this value won't be 0.5, the halfway point between 0 and 1.

If you want something like that, you will need to standardize your data prior to running the above algorithm:

Something like:

[ x - mean(X) ] / std(X)

This will center your data around 0. If you then apply the first function (normalization), it should now be centered around 0.5 (even if it is not normally distributed).