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 ...

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A good way to see where this article is headed is to take a look at the screen shot of a demo program shown in Figure 1. The demo sets up a dummy dataset of six items: [ 5.1 3.5 1.4 0.2] [ 5.4 3.9 1.7 ...

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 ...