Data Anomaly Detection Defined
Data anomaly detection is the process within data mining, that identifies unexpected events, data points, and observations that deviate from a dataset’s normal behavior.
Anomaly detection has two basic assumptions:
- Anomalies only occur very rarely in the data.
- Their features differ from the normal instances significantly.
There are three different types of anomalies.
- Point anomalies: A single instance of data which has deviated too far off from the rest of the data.
- Contextual anomalies: The abnormality is context specific.
- Collective anomalies: A set of data instances which collectively help in detecting anomalies.
Some anomaly detection techniques include:
- Density-based Anomaly Detection
- Clustering Anomaly Detection
- Time Series Data Anomaly Detection – Depending on a users business model and use case, time series data anomaly detection can be used for valuable metrics such as:
- Web page views
- Daily active users
- Cost per click
- Bounce rate
- Churn rate
- Revenue per click
- Average order value
In Data Defined, we help make the complex world of data more accessible by explaining some of the most complex aspects of the field.
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