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:

1. Anomalies only occur very rarely in the data.
2. 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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