Association Rules and the Apriori Algorithm

Association Rules and the Apriori Algorithm
or
Market Basket Analysis


Introduction to APRIORI

Apriori is an algorithm used for Association Rule Mining. It searches for a series of frequent sets of items in the datasets. It builds on associations and correlations between the item sets.

What is Associate Rule Mining?
ARM( Associate Rule Mining) is one of the important techniques in data science. In ARM, the frequency of patterns and associations in the dataset is identified among the item sets then used to predict the next relevant item in the set. This ARM technique is mostly used in business decisions according to customer purchases.
Example: In Walmart, if Ashok buys Milk and Bread, the chances of him buying Butter are predicted by the Associate Rule Mining technique.

Association Rule Mining

Association rules analysis is a technique to uncover how items are associated to each other. here are two elements of these rules:
Antecedent (IF): This is an item/group of items that are typically found in the Itemsets or Datasets.
Consequent (THEN): This comes along as an item with an Antecedent/group of Antecedents.
So before we understand the Apriori Algorithm, let’s understand the math behind it. There are 3 ways to measure association:

Support: It gives the fraction of transactions which contains item A and B. Basically Support tells us about the frequently bought items or the combination of items bought frequently.