Description: Global Outlier Detection is an advanced approach in the field of anomaly detection that focuses on identifying data points that significantly deviate from the overall behavior of a dataset. Unlike traditional methods that analyze local neighborhoods or specific patterns, this method considers the entire dataset, allowing for a more holistic and accurate assessment. This approach is particularly useful in contexts where data may have multiple dimensions and where interactions between variables can be complex. Global Outlier Detection employs various algorithms and statistical techniques to identify unusual patterns that may indicate errors, fraud, or rare events. Its relevance lies in its ability to enhance data quality and decision-making, as it enables organizations to detect issues before they escalate into crises. Furthermore, this method is applied across various industries, from finance to healthcare, where early identification of anomalies can significantly impact efficiency and safety.
