Market basket analysis is a data mining technique used by retailers to increase sales by better understanding customer purchasing patterns. It involves analyzing large data sets, such as purchase history, to reveal product groupings, as well as products that are likely to be purchased together.
Source: UofT
Frequent itemsets are those items whose support is greater than the threshold value or user-specified minimum support. It means if A & B are the frequent itemsets together, then individually A and B should also be the frequent itemset.
Suppose there are the two transactions: A= {1,2,3,4,5}, and B= {2,3,7}, in these two transactions, 2 and 3 are the frequent itemsets.
Below are the steps for the apriori algorithm:
Step-1: Determine the support of itemsets in the transactional database, and select the minimum support and confidence.
Step-2: Take all supports in the transaction with higher support value than the minimum or selected support value.
Step-3: Find all the rules of these subsets that have higher confidence value than the threshold or minimum confidence.
Step-4: Sort the rules as the decreasing order of lift.