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Detecting anomalous patterns in pharmacy retail data Rajeshkumar R. Sabhnani1, Daniel B. Neill1, Andrew W. Moore1, Punching Tsui2, Michael M. Wagner2, Jeremy U. Espino2 1 School of Computer Science,
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Detecting anomalous patterns is the process of identifying unusual or abnormal patterns in data.
The individuals or organizations responsible for data analysis and monitoring are typically required to file detecting anomalous patterns.
Filling out detecting anomalous patterns typically involves analyzing data using statistical methods or machine learning algorithms to identify patterns that deviate from normal behavior.
The purpose of detecting anomalous patterns is to identify potential irregularities or anomalies that may indicate fraud, security breaches, or other abnormal activities.
The specific information that needs to be reported on detecting anomalous patterns may vary depending on the context, but it typically includes the identified anomalies, the time and location of occurrence, and any relevant supporting data.
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