# Anomaly detection

> In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. Such examples may arouse suspicions […]

In data analysis, **anomaly detection** (also referred to as **outlier detection** and sometimes as **novelty detection**) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data.

Anomaly detection finds application in many domains including cybersecurity, medicine, machine vision, statistics, neuroscience, law enforcement and financial fraud to name only a few. Anomalies were initially searched for clear rejection or omission from the data to aid statistical analysis, for example to compute the mean or standard deviation. They were also removed to better predictions from models such as linear regression, and more recently their removal aids the performance of machine learning algorithms. However, in many applications anomalies themselves are of interest and are the observations most desirous in the entire data set, which need to be identified and separated from noise or irrelevant

…

*Source: [Wikipedia](https://en.wikipedia.org/wiki/Anomaly_detection)*

---

## Metadata

- **URL:** https://wpsearchai.com/anomaly-detection/
- **Published:** 2026-01-28T18:47:33+00:00
- **Modified:** 2026-01-28T18:47:33+00:00
- **Author:** admin
- **Categories:** Machine learning