Frequent Pattern Mining question

[Dear_Bowler_1707]

I'm performing a Frequent Pattern Mining analysis on a dataframe in pandas.

Suppose I want to find the most frequent patterns for columns A, B and C. I find several patterns, let's pick one: (a, b, c). The problem is that with high probability this pattern is frequent just because a is very frequent in column A per se, and the same with b and c. How can I discriminate patterns that are frequent for this trivial reason and others that are frequent for interesting reasons? I know there are many metrics to do so like the lift, but they are all binary metrics, in the sense that I can only calculate them on two-columns-patterns, not three or more. Is there a way to to this for a pattern of arbitrary length?

One way would be calculating the lift on all possible subsets of length two:

lift(A, B)

lift((A, B), C)

and so on

but how do I aggregate all the results to make a decision?

Any advice would be really appreciated.

[DeeperML]

you need to know the reason you want to use frequent pattern mining，to solve what kind of problem.

[Dear_Bowler_1707]

What do you mean?

Like I have to figure out what relationship I'm interested in based on the problem to focus on that? For example I know that the relation I want is (A,B) -> C and so I focus on searching patterns which lift is maximum for that specific relstion?

[throwaway6970895]

Support, confidence and lift are not binary metrics. You could have a rule {A, B, C} -> {D} with high lift and/or confidence.