Handling Missing Values — At Least Two Methods

1. Ignore the tuple — Simply discard records with missing values. Only reasonable when the missing attribute is critical (e.g., the class label) and the dataset is large enough that dropping a few rows doesn't hurt.

2. Fill in the missing value manually — A domain expert examines and fills each gap. Accurate but extremely slow and impractical for large datasets.

3. Use a global constant — Replace all missing values with a fixed label like "Unknown" or −∞. Simple, but the mining algorithm may mistakenly treat "Unknown" as a meaningful, interesting category.

4. Use the attribute mean/median — Replace missing numeric values with the mean (or median for skewed data) of that attribute across all tuples.

5. Use the mean/median of the same class — Replace with the mean of tuples belonging to the same class as the tuple with the missing value (more accurate than a global mean).

6. Use the most probable value — Predict the missing value using inference methods like regression, decision-tree induction, or Bayesian formalism — the most sophisticated but most accurate approach.

Missing Value
     │
     ├── Ignore tuple ─────────────▶ (drop row)
     ├── Manual fill ───────────────▶ (expert input)
     ├── Global constant ───────────▶ "Unknown"
     ├── Attribute mean/median ─────▶ statistical fill
     ├── Class-wise mean ───────────▶ context-aware fill
     └── Predicted value (regression/tree/Bayes) ─▶ model-based fill