Practical Machine Learning: A New Look at Anomaly Detection
Practical Machine Learning: A New Look At Anomaly Detection
by Ted Dunning and Ellen Friedman
Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. In this ebook, two committers of the Apache Mahout project use practical examples to explain how the underlying concepts of anomaly detection work.
From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data.
The concepts described in this ebook will help you tackle anomaly detection in your own project.
Use probabilistic models to predict what’s normal and contrast that with what you observe
Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm
Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model
Use historical data to discover anomalies in sporadic event streams, such as web traffic
Learn how to use deviations in expected behavior to trigger fraud alert