28 lines
1.1 KiB
Markdown
28 lines
1.1 KiB
Markdown
# Introduction to Association Rules
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**Association Rule Mining** is a technique to find relationships between items in a large dataset.
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- **Classic Example**: "Market Basket Analysis" - finding what products customers buy together.
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- *Example*: "If a customer buys **Bread**, they are 80% likely to buy **Butter**."
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## Key Concepts
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### 1. Itemset
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- A collection of one or more items.
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- *Example*: `{Milk, Bread, Diapers}`
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### 2. Support (Frequency)
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- How often an itemset appears in the database.
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- **Formula**:
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$$ \text{Support}(A) = \frac{\text{Transactions containing } A}{\text{Total Transactions}} $$
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- *Example*: If Milk appears in 4 out of 5 transactions, Support = 80%.
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### 3. Confidence (Reliability)
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- How likely item B is purchased when item A is purchased.
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- **Formula**:
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$$ \text{Confidence}(A \to B) = \frac{\text{Support}(A \cup B)}{\text{Support}(A)} $$
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- *Example*: If Milk and Bread appear together in 3 transactions, and Milk appears in 4:
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- Confidence(Milk -> Bread) = 3/4 = 75%.
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### 4. Frequent Itemset
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- An itemset that meets a minimum **Support Threshold** (e.g., must appear at least 3 times).
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