DMCT-NOTES/unit 3/01_Association_Rule_Mining.md

Introduction to Association Rules

Association Rule Mining is a technique to find relationships between items in a large dataset.

• Classic Example: "Market Basket Analysis" - finding what products customers buy together.
• Example: "If a customer buys Bread, they are 80% likely to buy Butter."

Key Concepts

1. Itemset

• A collection of one or more items.
• Example: {Milk, Bread, Diapers}

2. Support (Frequency)

• How often an itemset appears in the database.
• Formula: \text{Support}(A) = \frac{\text{Transactions containing } A}{\text{Total Transactions}}
• Example: If Milk appears in 4 out of 5 transactions, Support = 80%.

3. Confidence (Reliability)

• How likely item B is purchased when item A is purchased.
• Formula: \text{Confidence}(A \to B) = \frac{\text{Support}(A \cup B)}{\text{Support}(A)}
• Example: If Milk and Bread appear together in 3 transactions, and Milk appears in 4:
  • Confidence(Milk -> Bread) = 3/4 = 75%.

4. Frequent Itemset

• An itemset that meets a minimum Support Threshold (e.g., must appear at least 3 times).