I am working on a Retail Company's in-store transactions for 3 months. I have performed the Market Basket Analysis on the same and I'm getting hundreds if not thousands of association rules. I am using the apriori algorithm from
mlxtend.frequent_patterns import apriori in Python and I have used different support values in
apriori(basket_sets, min_support=0.01, use_colnames=True), all the way from 0.01 to 0.4.
If I use a support value too high, (for some stores there are no rules found), there are very few association rules, and if I use a support value too low, its very difficult to make sense of the association rules generated since there are too many of such rules.
Since I've chosen to go with the low support value, I wanted to understand ways in which I could present the rules which would make business sense to the data owners. If there is any literature (I've tried googling at least 10 different queries but all of them return "How to do Market Basket Analysis and its applications!") on how to present the Association results, that would be really good.