There are now other algorithms for basket analysis that run faster than Apriori. The code we saw earlier was simple, and it was good enough for us, as we only had about 100,000 transactions. If we had many millions, it might be worth using a faster algorithm. Note, though, that learning association rules can often be done offline, where efficiency is not as great a concern.

There are also methods that you can use to work with temporal information, leading to rules that take into account the order in which you have made your purchases. Consider, as an example, that someone buying supplies for a large party may come back for trash bags. It may make sense to propose trash bags on the first visit. However, it would not make sense to propose party supplies to everyone who buys a trash bag.