# 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).