Data Preprocessing - created from Mind Map

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Data Transformation

Data Cleaning

Data Reduction

Data Integration

Smoothing

Aggregation

Generalization

Normalization

Attribute construction

Noisy Data

Missing values

Removing Outliers

Resolve inconsistency

Binning

Regression

Clustering

Ignore the tuple

Fill the missing values manually

Global constant to fill the missing values

Use attribute mean to fill missing value

Use most probable value to fill missing value

Dimensionality Reduction

Numerosity Reduction

Data Compression

Entity Identification problem

Redundancy and correlation analysis

Detecting and resolving data value conflicts

X^2 correlation test for nominal data

Correlation coefficient for numeric data

Covariance for numeric data