Overall methodology

To perform association rule mining for affinity analysis, we first use the Apriori algorithm to generate frequent itemsets. Next, we create association rules (for example, if a person recommended movie X, they would also recommend movie Y) by testing combinations of premises and conclusions within those frequent itemsets.

1. For the first stage, the Apriori algorithm needs a value for the minimum support that an itemset needs to be considered frequent. Any itemsets with less support will not be considered.

Setting this minimum support too low will cause Apriori to test a larger number of itemsets, slowing the algorithm down. Setting it too high will result in fewer itemsets being considered frequent.

1. In the second stage, after the frequent itemsets have been discovered, association rules are tested based on their confidence. We could choose a minimum confidence level, a number of rules to return, or simply return all of them and let the user decide what to do with them.

In this chapter, we will return only rules above a given confidence level. Therefore, we need to set our minimum confidence level. Setting this too low will result in rules that have a high support, but are not very accurate. Setting this higher will result in only more accurate rules being returned, but with fewer rules being discovered overall.