Data anomaly detection is the process of examining a set of source data to find data items that are different in some way from the majority of the source items. There are many different types of ...

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Anomaly detection, also called outlier detection, is the process of finding rare items in a dataset. Examples include finding fraudulent login events and fake news items. Take a look at the demo ...

Anomaly detection is the process of identifying events or patterns that differ from expected behavior. Anomaly detection can range from simple outlier detection to complex machine learning algorithms ...

Unlike pattern-matching, which is about spotting connections and relationships, when we detect anomalies we are seeing disconnections—things that do not fit together. Anomalies get much less attention ...