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Data Preprocessing

1. Why Preprocess the Data?

2. Data Cleaning

   • Missing Values
     •    1.Ignore the tuple  2.Fill in the missing value manually 3.Use a global constant to fill in the missing value 4.Use the attribute mean to fill in the missing value  5.Use the attribute mean for all samples belonging to the same class as the given tuple Use the most probable value to fill in the missing value   
   • Noisy Data
     •    1.Binning 2.Regression 3.Clustering
   • Data Cleaning as a Process

3. Data Integration and Transformation

   • Data Transformation
     • 1.Smoothing 2.Aggregation  3.Generalization4.Normalization5.Attribute 6.construction  
   • Data Integration

4. Data Reduction

   • Data Cube Aggregation
   • Attribute Subset Selection
     •   1.Stepwise forward selection 2.Stepwise backward elimination 3.Combination of forward selection   and backward elimination 4.Decision tree induction   
   • Dimensionality Reduction
   • Numerosity Reduction

5. Data Discretization and Concept Hierarchy Generation

   • Discretization and Concept Hierarchy Generation for Numerical Data
   • Concept Hierarchy Generation for Categorical Data