Have you ever seen the message “People who bought this also bought that” while shopping online?
That’s an example of association rule mining — a technique used in data mining to find patterns between items in large datasets.
Let’s understand how it works in simple words.
What Are Association Rules?
Association rules are like “if-then” statements that show how items are related.
Example:
If a customer buys bread, they often buy butter.
This helps companies understand buying behaviour and design better recommendations. It’s used in retail, e-commerce, banking, telecom, and even healthcare.
Metrics That Make It Work
While building these rules, three basic measures are used:
Support: How often the items appear together in the dataset
Confidence: How often item B appears when item A appears
Lift: How strongly item A and item B are connected
Types of Association Rules
Single-dimensional: Items from the same category (Milk → Bread)
Multidimensional: From different attributes (Age 20-30 → Buys Protein Powder)
Boolean: Yes/No based
Quantitative: Based on numerical values (Income > 50K → Buys SUV)
Why It’s Useful
Association rule mining helps businesses build better recommendation systems, detect fraud patterns, and plan marketing strategies.
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