Anomaly Detection

Anomaly detection, also known as outlier detection, is the process of identifying data points, events, or observations that deviate significantly from the normal behavior of a dataset. These anomalous instances are often referred to as anomalies, outliers, novelties, or exceptions. Anomaly detection is a critical task in many domains because these rare events can signify important information, such as a credit card fraud, a network intrusion, a system failure, or a medical symptom. There are various approaches to anomaly detection, many of which are unsupervised. Statistical methods might assume that the normal data points follow a certain distribution (e.g., a Gaussian distribution) and flag any point that has a low probability under that distribution as an anomaly. Distance-based methods, like those using k-NN, identify a point as an anomaly if it is far away from its neighbors. Clustering-based methods assume that normal data points belong to large, dense clusters, while anomalies are isolated points that do not belong to any cluster. More advanced techniques like Isolation Forests or One-Class SVMs are specifically designed for this task. The choice of algorithm depends on the nature of the data and the type of anomalies one expects to find. It's a powerful unsupervised technique for finding the 'needles in the haystack' within large datasets.