How to present Market Basket Analysis Results?

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.

Thanks

Topic python-3.x market-basket-analysis machine-learning

Category Data Science

1
answered at

The paper linked by bkshi looks like an excellent resource for visualization. I don't know if you considered any metrics other than support, but filtering on confidence and lift is also supported by the mlxtend package and might also help you narrow down your rules in terms of value to the business.

1
answered at

The best way to understand multiple association rules is to visualize them. This makes it even easier to present. This paper covers multiple approaches for visualizing association rules. Go through its references. They also suggest their tool, but it is in R. If you want resources for python try searching for "association rules visualization python" and you'll find some resources.

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