Description: Extremum seeking is an optimization technique used to identify outliers or anomalies in data sets. This technique is based on the premise that extremes, whether maximum or minimum, can indicate unusual or exceptional behaviors within a data set. In the context of anomaly detection, extremum seeking allows analysts and data scientists to locate points that deviate significantly from the norm, which can be crucial for identifying fraud, system failures, or unexpected behaviors in various processes. The technique can be implemented through various algorithms and artificial intelligence models that analyze patterns and distributions in the data to determine which values are considered extreme. The relevance of this technique lies in its ability to improve data quality and decision-making, enabling organizations to proactively respond to situations that could compromise their integrity or efficiency. In summary, extremum seeking is a powerful tool in the artificial intelligence arsenal for anomaly detection, providing a systematic approach to identifying and analyzing data that does not fit established expectations.
