Outlier Detection in Data Mining

Outlier detection in data mining involves identifying data points that significantly differ from the majority of data. These unusual points can indicate errors, fraud, or rare events. For example, in banking, a sudden large transaction might be an outlier signaling potential fraud. Detecting outliers helps improve data quality, uncover hidden insights, and support decision-making. Techniques involve analyzing patterns, distances, or statistical methods to distinguish typical data from anomalies. Overall, it's about recognizing data that doesn't fit the normal pattern, enabling better understanding and responses to unusual situations.