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Sample records for unsupervised anomaly detection
What is anomaly detection in data science?Anomaly detection (aka outlier analysis) is a step in data mining that identifies data points, events, and/or observations that deviate from a dataset's normal behavior. Anomalous data can indicate critical incidents, such as a technical glitch, or potential opportunities, for instance, a change in consumer behavior.
What is the difference between anomaly and outlier?Outliers are observations that are distant from the mean or location of a distribution. However, they don't necessarily represent abnormal behavior or behavior generated by a different process. On the other hand, anomalies are data patterns that are generated by different processes.
What is an anomaly in data?What are Anomalies? Anomalies are data points that stand out amongst other data points in the dataset and do not confirm the normal behavior in the data. These data points or observations deviate from the dataset's normal behavioral patterns.
Is the process of finding data objects that are very different from expectation with behaviors?Outlier detection (also known as anomaly detection) is the process of finding data objects with behaviors that are very different from expectation. Such objects are called outliers or anomalies.
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