A Review of Outlier Detection Methods: From Visual Inspection to Advanced Algorithms

Outlier detection is a fundamental component of data preprocessing that aims to identify anomalies that may arise from measurement errors, data entry errors, system failures, fraudulent activities, or rare but meaningful events. The aim of this study is to provide a comprehensive overview of outlier detection methods, ranging from traditional statistical and visualization techniques to advanced machine learning and deep learning approaches.

This review presents a structured taxonomy of outlier detection methodologies, progressing from fundamental visual methods (box plots, scatter plots, histograms) through statistical methods (parametric: Z-score, regression-based; non-parametric: kernel density estimation), distance-based methods (local neighborhood, kNN-distance), density-based techniques (LOF, COF, INFLO, MDEF), and clustering-based approaches (k-means, hierarchical, DBSCAN). We extend coverage to advanced algorithms including Isolation Forest, Feature Bagging, and deep learning-based autoencoders, with a simulated medical case study and reproducible R code.

No universally superior method exists; performance is highly context-dependent. Advanced algorithms demonstrate superior scalability and effectiveness in high-dimensional and complex data environments, albeit with increased computational requirements and reduced interpretability. In our simulated medical case study, Isolation Forest achieved the highest sensitivity (0.97) for detecting injected anomalies.

This study shows that no single outlier detection technique is suitable for all scenarios. The selection of an appropriate method should be done considering the data structure, dimensions, and specific requirements for reliable anomaly detection.

Methodological selection requires careful consideration of data properties, computational constraints, and analytical goals. We provide a practical, reproducible framework integrating domain expertise with modern algorithmic approaches.