I'm working on a classification project. I have many rows, containing many binary attributes, some of which are often appearing together, exactly like what we can encounter in the Market Basket problem (in which you can, for example identify, that if you buy 'Milk' to a supermarket, you also have a more than random chance to buy 'Eggs').
My idea is then to take my target as an attribute, extract best Item-set containing my target (so having
Target=1, exactly like my previous example if I wanted to make a model to predict whether the client will buy eggs or not).
Then, looking at the
fit of each item-set, I'll have the info of which groups of products have best chances to make my target be 1.
Even though the method looks good, I have a problem. I'm wondering if there's a way to only extract item-sets with target on them.
All articles I found about making classification based on association rules explain that one of the big downside of the method is time taken, because it has to find all item-sets, and after removing all the ones that are not including target.
Is there a way, using item-set properties, to specify the algorithm (Apriori, FP-Growth, etc.) to only calculate Item-sets including Target, and not waste time calculating all item-sets?