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Anomaly detection is used to identify outliers in data from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Anomaly detection is a common data mining technique used to identify unusual or rare examples in a dataset. Anomalies, or outliers, are data points that do not conform to the general patterns in the data. These anomalies can be caused by errors in data collection, measurement noise, or actual interesting phenomena that are worth further investigation. Identifying outliers is important in many real-world applications. For example, in fraud detection, anomalies in credit card transactions may indicate potential fraudulent activity. In network security, anomalies in network traffic may indicate a security breach. In manufacturing, anomalies in process data may indicate a faulty machine or process. There are d...
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