Finding Data Anomalies You Didn't Know to Look For
Anomaly 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 not know 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 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 report will help you tackle anomaly detection in your own project.
Use probabilistic models to predict what’s normal and contrast that to 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 alerts
Practical Machine Learning: A New Look at Anomaly Detection
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. Ted is on Twitter at @ted_dunning.
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.
Comments about oreilly Practical Machine Learning: A New Look at Anomaly Detection:
Just finished reading this "book". Should really just be an article in a magazine. Provides no real guidance on how to collect and analyze data through machine learning. The authors show pictures of anomalies and describe how to visually see the anomaly (and how you can miss it), but neglect tying in how to use any form of machine learning to provide the detection. A few links to github are provided and are probably what the authors consider useful, but it would be a better "look" to have really described the example and walked the reader through the application of machine learning to the example.
Overall, glad I got this on sale for half price, but even at at that it is still not worth the money and especially the time to read it.
Bottom Line No, I would not recommend this to a friend
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