Unsupervised anomaly detection is a technique used to identify patterns or instances in data that deviate significantly from the norm without requiring labeled data for training. In traditional supervised learning, models learn from input data that comes with predefined labels indicating whether each instance is normal or anomalous. In contrast, unsupervised methods analyze the structure and distribution of the input data itself, allowing for the discovery of anomalies without explicit guidance. This approach is especially useful when labeled datasets are scarce or when anomalies are not well-defined in advance.
One common method of unsupervised anomaly detection is clustering, which groups similar data points together. When a new instance is analyzed, if it doesn’t fit well into any of the established clusters, it may be flagged as an anomaly. For example, in a network traffic monitoring scenario, normal user activity could be clustered based on behavior patterns. If a new activity occurs that doesn't match any of these clusters—such as an unusually large amount of data being transferred—it could be identified as potentially malicious or an anomaly.
Another approach involves statistical methods, where the model learns the distribution of the data and identifies points that fall outside a certain threshold. For instance, if a dataset representing temperature readings typically ranges from 20 to 30 degrees Celsius, a reading of 15 degrees might be flagged as anomalous. This method is useful for detecting outliers in time series data, such as financial transactions where a sudden spike in spending might indicate fraud. Overall, unsupervised anomaly detection provides a flexible framework for identifying irregularities in various applications, from security to manufacturing.