What Is Association Rule in Data Mining?

If you’ve ever seen “Customers who bought this also bought that” while shopping online, you’ve already seen association rule mining in action.

It’s a technique in data mining that finds patterns between items in large datasets. Let’s break it down simply.

How Association Rules Work

Association rules are like “if-then” statements.

Example:
If a customer buys bread, they often buy butter too.

These rules help companies understand how people buy products together. They are widely used in retail, e-commerce, banking, healthcare, and telecom.

Three Key Metrics

When creating these rules, three simple measures are used:

• Support: How often an item appears in the data

• Confidence: How often item B is bought when item A is bought

• Lift: How strongly item A and item B are related

Different Types of Association Rules

• Single-dimensional: Items from the same category (Milk → Bread)

• Multidimensional: Items from different attributes (Age 20–30 → Buys Protein Powder)

• Boolean: Yes/No based rules

• Quantitative: Based on numbers (Income > 50K → Buys SUV)

Why It Matters

Association rule mining helps build recommendation systems, fraud detection models, and marketing strategies.

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