Sin título | Mapa Mental

Data Preprocessing

Why Preprocess the Data?
Data Cleaning
1. Missing Values
  Nota:
  - 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
2. Noisy Data
  Nota:
  - 1.Binning 2.Regression 3.Clustering
3. Data Cleaning as a Process
Data Integration and Transformation
1. Data Transformation
  Nota:
  - 1.Smoothing 2.Aggregation 3.Generalization4.Normalization5.Attribute 6.construction
2. Data Integration
Data Reduction
1. Data Cube Aggregation
2. Attribute Subset Selection
  Nota:
  - 1.Stepwise forward selection 2.Stepwise backward elimination 3.Combination of forward selection and backward elimination 4.Decision tree induction
3. Dimensionality Reduction
4. Numerosity Reduction
Data Discretization and Concept Hierarchy Generation
1. Discretization and Concept Hierarchy Generation for Numerical Data
2. Concept Hierarchy Generation for Categorical Data

Resumen del Recurso

	Creado por siva.sivakumar201328 hace alrededor de 9 años