6 min readAug 2, 2025
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Anomaly detection is one of those concepts in machine learning that looks deceptively simple but has a huge impact in real-world applications — from fraud prevention to equipment maintenance, from healthcare diagnosis to cybersecurity.
This guide covers everything you need to know: the theory, intuition, algorithms, and mathematics behind anomaly detection. We’ll explore three key algorithms in detail — Isolation Forest, DBSCAN, and Local Outlier Factor (LOF) — and discuss when and why you’d use each.
Introduction
Anomaly detection is the process of identifying unusual data points, patterns, or events that deviate significantly from the majority of the data. While standard preprocessing often removes outliers, anomaly detection focuses on finding them…
6 min readAug 2, 2025
–
Anomaly detection is one of those concepts in machine learning that looks deceptively simple but has a huge impact in real-world applications — from fraud prevention to equipment maintenance, from healthcare diagnosis to cybersecurity.
This guide covers everything you need to know: the theory, intuition, algorithms, and mathematics behind anomaly detection. We’ll explore three key algorithms in detail — Isolation Forest, DBSCAN, and Local Outlier Factor (LOF) — and discuss when and why you’d use each.
Introduction
Anomaly detection is the process of identifying unusual data points, patterns, or events that deviate significantly from the majority of the data. While standard preprocessing often removes outliers, anomaly detection focuses on finding them because these rare cases can be the most valuable signals.
Key Idea: Outliers are rare events that differ greatly from the norm. They may represent problems, opportunities, or critical insights.
Outliers vs Anomalies
While the terms are often used interchangeably, there’s a subtle difference:
- Outlier: A data point that lies far from most other points in a statistical sense. It is purely based on mathematical deviation from the distribution.
- Anomaly: A data point that is unusual or suspicious in the context of the specific problem domain. Not every statistical outlier is…