Amaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may t kw what the puzzle is, much less what suspects you're looking for. This O'Reilly report uses practical examples to explain how the underlying concepts of amaly detection work. From banking security to natural sciences, medicine, and marketing, amaly detection has many useful applications in this age of big data. And the search for amalies will intensify once the Internet of Things spawns even more new types of data. The concepts described in this report will help you tackle amaly detection in your own project. Use probabilistic models to predict what's rmal and contrast that to what you observe Set an adaptive threshold to determine which data falls outside of the rmal range, using the t-digest algorithm Establish rmal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model Use historical data to discover amalies in sporadic event streams, such as web traffic Learn how to use deviations in expected behavior to trigger fraud alerts
Ted Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects and mentor for these Apache projects: Spark, Storm, Stratosphere, and Datafu. He contributed to Mahout clustering, classification, and matrix decomposition algorithms and helped expand the new version of Mahout Math library. Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems, built fraud-detection systems for ID Analytics (LifeLock), and has issued 24 patents to date. Ted has a PhD in computing science from University of Sheffield. When he's not doing data science, he plays guitar and mandolin. Ellen Friedman is a consultant and commentator, currently writing mainly about big data topics. She is a committer for the Apache Mahout project and a contributor to the Apache Drill project. With a PhD in Biochemistry, she has years of experience as a research scientist and has written about a variety of technical topics including molecular biology, nontraditional inheritance, and oceanography. Ellen is also co-author of a book of magic-themed cartoons, A Rabbit Under the Hat. Ellen is on Twitter at @Ellen_Friedman.