Association Rule Learning on a List of Lists
Date: March 11, 2014 04:41AM
Hi Everyone,
New to the forum, glad to be here. Have this question that I could use some help with.
Let's say we have a list of lists of objects, which are selected from a predetermined set (we can also look at each list of objects as a transaction, like they have in the Apriori algorithm). Each object can only appear in each list once.
What needs to be done, is to create a coefficient which describes the compatibility of each variable in the predetermined set to each list of objects. This should be done by the frequency of this object in other lists containing objects similar to the ones on the list for which the coefficient is made for.
The output result for each coefficient should be a value between 0 and 1, with 1 meaning the object should definitely be part of the list (or already in it) and 0 means there was no indication of compatibility.
This simple example might help explain the problem:
Let's say we have the following lists:
1: {1, 2, 3, 4}
2: {1, 2}
3: {1, 2, 3}
4: {1}
And we want to know how likely is it that 1, 2, 3 or 4 will be in list #4; 1 would be the highest (1), since it's in the list; 2 would be second-highest since it's in all other lists containing 1; 3 would be the next since it's only in list #1 and list #3 and 4 would be the last since it's only in list #1.
The algorithm should probably also take a lot into account (like - what if another list has only part of the list for which the coefficient is made, etc.).
Read about the Apriori algorithm, but I don't think I can use it - as it answers a different question. Since this seems like a rather general problem, I bet there is somewhere a good solution for it I can utilise - but I could use some direction.
Thanks in advanced.