= Anomaly Detection at Multiple Scales
{wiki=Anomaly_Detection_at_Multiple_Scales}
Anomaly detection at multiple scales refers to the practice of identifying unusual patterns or outliers in data that may occur at various levels of granularity or resolution. This approach is particularly useful in complex datasets where anomalies can manifest differently depending on the perspective or the scale of analysis. \#\#\# Key Concepts: 1. **Multi-scale Analysis**: - In many datasets, anomalies can be evident at different scales, such as local versus global patterns.
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