Association Rule in data mining refers to a technique used to find relationships or patterns between different items in large datasets. It identifies how the occurrence of one item is associated with the occurrence of another. This is commonly used in market basket analysis, where the goal is to find associations between products purchased together.

Structure of Association Rule:

An association rule typically has the following form: A → B

Where:

• A is the antecedent (left-hand side or "if" part),
• B is the consequent (right-hand side or "then" part).

The rule means that if item A is purchased, then item B is likely to be purchased as well.

A real-world example of association rule mining can be seen in retail or supermarket settings, where it is used to improve product recommendations, sales, and inventory management. One of the most common applications of association rule mining is in market basket analysis, where retailers analyze the items customers typically buy together.

Example: Supermarket Market Basket Analysis

Imagine a supermarket chain that wants to use association rule mining to discover patterns in customer purchases. The goal is to increase sales by offering targeted promotions or by optimizing product placement on store shelves.