Data Cleaning

6.

Techniques for Correcting Inaccurate Data

6.1.

Outlier Detection Methods

6.1.1.

Statistical Approaches

6.1.1.1.

6.1.1.1.1.

Standard Z-Score

6.1.1.1.2.

Modified Z-Score

6.1.1.1.3.

6.1.1.2.

Interquartile Range (IQR) Method

6.1.1.2.1.

6.1.1.2.2.

6.1.1.2.3.

6.1.1.3.

6.1.1.4.

6.1.1.5.

Chauvenet's Criterion

6.1.2.

Distribution-Based Methods

6.1.2.1.

Gaussian Distribution Assumptions

6.1.2.2.

Non-Parametric Methods

6.1.2.3.

Quantile-Based Detection

6.1.2.4.

Percentile Methods

6.1.3.

Machine Learning Approaches

6.1.3.1.

Isolation Forest

6.1.3.2.

6.1.3.3.

Local Outlier Factor (LOF)

6.1.3.4.

DBSCAN Clustering

6.1.4.

Multivariate Outlier Detection

6.1.4.1.

Mahalanobis Distance

6.1.4.2.

Principal Component Analysis (PCA)

6.1.4.3.

Minimum Covariance Determinant

6.1.4.4.

Robust Covariance Estimation

6.1.5.

Time Series Outlier Detection

6.1.5.1.

Seasonal Decomposition

6.1.5.2.

ARIMA Residuals

6.1.5.3.

6.1.5.4.

Change Point Detection

6.1.6.

Visualization-Based Detection

6.1.6.1.

6.1.6.2.

6.1.6.3.

Histogram Analysis

6.1.6.4.

6.1.6.5.

Parallel Coordinates

6.1.7.

Domain-Specific Methods

6.1.7.1.

Business Rule Validation

6.1.7.2.

Expert Knowledge Integration

6.1.7.3.

Historical Pattern Analysis

6.1.7.4.

Contextual Validation

6.2.

Outlier Treatment Strategies

6.2.1.

Capping and Winsorization

6.2.1.1.

Percentile Capping

6.2.1.2.

Standard Deviation Capping

6.2.1.3.

IQR-Based Capping

6.2.1.4.

Asymmetric Winsorization

6.2.2.

Transformation Techniques

6.2.2.1.

Log Transformation

6.2.2.2.

Square Root Transformation

6.2.2.3.

Box-Cox Transformation

6.2.2.4.

Yeo-Johnson Transformation

6.2.2.5.

Inverse Transformation

6.2.3.

Removal Strategies

6.2.3.1.

Complete Removal

6.2.3.2.

Conditional Removal

6.2.3.3.

Iterative Removal

6.2.3.4.

Cluster-Based Removal

6.2.4.

Replacement Methods

6.2.4.1.

Mean Replacement

6.2.4.2.

Median Replacement

6.2.4.3.

Mode Replacement

6.2.4.4.

Interpolated Values

6.2.4.5.

Predicted Values

6.2.5.

6.2.5.1.

Robust Statistics

6.2.5.2.

6.2.5.3.

Median Absolute Deviation

6.2.5.4.

Huber Loss Functions

6.3.

Error Correction Approaches

6.3.1.

Manual Correction

6.3.1.1.

6.3.1.2.

Data Steward Validation

6.3.1.3.

6.3.1.4.

Quality Control Processes

6.3.2.

Rule-Based Correction

6.3.2.1.

Business Logic Rules

6.3.2.2.

Validation Rules

6.3.2.3.

Constraint-Based Correction

6.3.2.4.

Pattern-Based Fixes

6.3.3.

Reference Data Validation

6.3.3.1.

External Database Lookup

6.3.3.2.

Master Data Management

6.3.3.3.

Authoritative Source Validation

6.3.3.4.

Cross-Reference Checking

6.3.4.

Automated Correction

6.3.4.1.

Machine Learning Models

6.3.4.2.

Pattern Recognition

6.3.4.3.

Similarity-Based Correction

6.3.4.4.

Probabilistic Correction

6.3.5.

Cross-Validation Methods

6.3.5.1.

Multiple Source Comparison

6.3.5.2.

Consensus-Based Correction

6.3.5.3.

Voting Mechanisms

6.3.5.4.

Confidence Scoring

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