Anomaly Detection
Techniques for identifying data points, events, or observations that deviate significantly from expected patterns. Anomalies can indicate fraud, equipment failure, security breaches, or other important events.
Why It Matters
Anomaly detection protects businesses from fraud, catches equipment failures before they are catastrophic, and identifies security intrusions in real time.
Example
A credit card system flagging a $5,000 purchase in a foreign country at 3 AM when the cardholder typically makes small domestic purchases during business hours.
Think of it like...
Like a lifeguard who scans the pool for anyone behaving differently from the normal swimmers — they are trained to spot the unusual amid the ordinary.
Related Terms
Unsupervised Learning
A type of machine learning where the model learns patterns from unlabeled data without being told what the correct output should be. The algorithm discovers hidden structures, groupings, or patterns in the data on its own.
Clustering
An unsupervised learning technique that groups similar data points together based on their characteristics, without predefined labels. The algorithm discovers natural groupings in the data.
Autoencoder
A neural network that learns to compress data into a lower-dimensional representation (encoding) and then reconstruct it back (decoding). It learns what features are most important for faithful reconstruction.