Association Rule Mining
Market Basket Analysis & Apriori Algorithm (Week#7-Intelligent System)
Proposed by Agrawal et al in 1993.
Initially used for Market Basket Analysis to find how items
purchased by customers are related.
Market Basket Analysis
In today’s world, the goal of any organization is to increase revenue. Can this be done by pitching just one product at a time to the customer? The answer is a clear no. Hence, organizations began mining data related to frequently bought items.
Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. They try to find out associations between different items and products that can be sold together, which gives assisting in the right product placement. Typically, it figures out what products are being bought together and organizations can place products in a similar manner. Let’s understand this better with an example:
People who buy Bread usually buy Butter too. The Marketing teams at retail stores should target customers who buy bread and butter and provide an offer to them so that they buy the third item, like eggs.
Association Rule Mining
Association rules can be thought of as an IF-THEN relationship. Suppose item A is being bought by the customer, then the chances of item B being picked by the customer too under the same Transaction ID is found out.
There 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.
There are 3 ways to measure association:
- Support
- Confidence
- Lift
Apriori Algorithm
Apriori algorithm uses frequent itemsets to generate association rules. It is based on the concept that a subset of a frequent itemset must also be a frequent itemset. Frequent Itemset is an itemset whose support value is greater than a threshold value(support).
