2071589
Descrição
Mapa Mental por siva.sivakumar201328, atualizado more than 1 year ago
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Criado por siva.sivakumar201328
aproximadamente 9 anos atrás
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Data Preprocessing
- Why Preprocess the Data?
- Data Cleaning
- Missing Values
Anotações:
- 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
Anotações:
- 1.Binning 2.Regression 3.Clustering
- Data Cleaning as a Process
- Missing Values
- Data Integration and Transformation
- Data Transformation
Anotações:
- 1.Smoothing 2.Aggregation 3.Generalization4.Normalization5.Attribute 6.construction
- Data Integration
- Data Transformation
- Data Reduction
- Data Cube Aggregation
- Attribute Subset Selection
Anotações:
- 1.Stepwise forward selection 2.Stepwise backward elimination 3.Combination of forward selection and backward elimination 4.Decision tree induction
- Dimensionality Reduction
- Numerosity Reduction
- Data Cube Aggregation
- Data Discretization and Concept Hierarchy Generation
- Discretization and Concept Hierarchy Generation for Numerical Data
- Concept Hierarchy Generation for Categorical Data
- Discretization and Concept Hierarchy Generation for Numerical Data
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