29 lines
1.1 KiB
Markdown
29 lines
1.1 KiB
Markdown
# Data Mining Techniques
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There are several key techniques used to mine data.
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## 1. Classification (Predictive)
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- **Goal**: Assign items to predefined categories (classes).
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- **Supervised Learning**: We know the categories beforehand.
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- **Example**: Is this email **Spam** or **Not Spam**?
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## 2. Regression (Predictive)
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- **Goal**: Predict a continuous **number**.
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- **Example**: Predicting the **price** of a house based on its size and location.
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## 3. Clustering (Descriptive)
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- **Goal**: Group similar items together.
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- **Unsupervised Learning**: We don't know the groups beforehand.
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- **Example**: Grouping customers into segments (e.g., "High Spenders", "Budget Shoppers").
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## 4. Association Rules (Descriptive)
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- **Goal**: Find relationships between items.
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- **Market Basket Analysis**: "People who buy Bread often also buy Butter."
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- **Key Terms**:
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- **Support**: How often items appear together.
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- **Confidence**: How likely item B is purchased if item A is purchased.
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## 5. Outlier Detection
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- **Goal**: Find unusual data points that don't fit the pattern.
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- **Example**: Detecting credit card fraud (a huge transaction in a usually quiet account).
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