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unit 3/01_Association_Rule_Mining.md

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+# 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).