Data Science
Anomaly Detection
May 11, 2017 Marko Mihin

Anomaly detection has countless practical applications, from intrusion detection (identifying irregular patterns in network traffic that could signal a hack) to system health monitoring (spotting a malignant tumor in an MRI scan), and fault detection in operating environments to fraud detection in financial and credit card transactions.

Matching algorithms are used to detect anomalies across a dataset by comparing a sequence with previously known models and profiles. Additional techniques are used to eliminate false alarms, reduce risks, and to make predictions or anticipate future occurrences in a dataset.

What is anomaly detection?
Anomaly detection is a form of classification that detects points in a dataset that do not conform to the expected pattern. Anomalies are not always bad or indicative of a failure. The goal of anomaly detection is to identify points that are unusual within a dataset that would otherwise be homogeneous.

There are three general methods of anomaly detection
Unsupervised anomaly detection techniques identify anomalies in an unlabeled test dataset under the assumption that the majority of the instances in the dataset are normal, and finding instances that seem fit least with the remainder of the dataset.

Supervised anomaly detection techniques require a dataset that has been labeled as "normal" and "abnormal" and involves training a classifier - the key difference with many other statistical classification problems being the inherent unbalanced nature of outlier detection.

Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training dataset, and then testing the likelihood of a test instance to be generated by the learnt model.

Why this is important for marketing

Anomaly detection is also an important tool to help identify and predict marketing trends. It's used to spot "What's Hot?" and "What's Next?” Anomaly detection helps you find the subtle changes in consumer buying habits that lead you in the right direction and help focus your marketing efforts around the most opportune topics and trends.

Marko Mihin