But this fixed,

predefined concept hierarchy may not fit your data distribution.

For example, in the university,

likely you may want to partition the age for students.

You may say it's 18 to 20, 20 to 22, or something like that.

But for income, you may say $10,000 is one partition or low and high.

But if you go to hospital,

their age distribution you may like to say middle age or old or young.

So another way is we do clustering based on data.

That means we take every dimension, we study their distribution of the age and

income, we perform certain clustering algorithm, generate a few clusters,

and then we find the parallelized frequent pattern of each such cluster of pairs.

Then finally there's also popular ways to do deviation analysis.

That means instead of doing fixed interval,

we may do based on certain condition like gender is female.

We may find their mean or a median or something, some statistic measure,

you will find if the wage mean is substantially deviated from

the overall mean, then this could be an interesting rule.

Let's go a little further to see how to find some such deviation.

We also call extraordinary or interesting phenomena.

Usually for this we may say the left-hand side is a subset of the population and

the right-hand side is some kind of extraordinary behavior expressed

using some statistical measure which could deviate from the overall.

Then the rule, whether is true rule or is just a very exceptional case,

we need to do some statistic test, like a Z-test,

to confirm whether such kind of rule is of high confidence.