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Practical Machine Learning: A New Look at Anomaly Detection, by Ted Dunning, Ellen Friedman
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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
- Sales Rank: #1507331 in Books
- Brand: Dunning, Ted/ Friedman, Ellen
- Published on: 2014-09-06
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .14" w x 6.00" l, .0 pounds
- Binding: Paperback
- 66 pages
About the Author
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.
Most helpful customer reviews
13 of 17 people found the following review helpful.
More of a "pamphlet" than a "book".
By hp
There are a lot of short, introductory texts and review articles out there that are really useful- they introduce you to the fundamental concepts of the field, so that you have a basic understanding and so that you'll know what to look up if you need it. This is not one of those books.
The depth of the "practical machine learning" advice in this book is at the level of gems like "before you can spot an anomaly, you first have to figure out what 'normal' is." (chapter 2) Really? My anomaly detection system will have to know what things AREN'T anomalies? Well thank God I dropped $18 to find that out.
Sure, the book (sort of) introduces some important concepts that could point you toward more information- like self-information, maximum entropy distributions, type I and II errors, and Bayes risk. I say "sort of" because they're not derived, motivated, or explained in any detail. Most importantly, the authors don't use the proper terms for any of them, so you won't even know what to look up for more information.
My favorite chapter is the one devoted to the "t-Digest" algorithm, which was developed by one of the authors. You get to spend the entire chapter waiting for the part where they explain the algorithm, what it does, or how it works. Guess what- it's not there! There's literally an entire chapter on an algorithm that never discusses, even qualitatively, what the algorithm is.
I honestly have no idea who this book is supposed to be for. The authors bring up Mahout constantly, which you're probably not using if you're new to machine learning. If you aren't a complete novice, though, you'll just be insulted. And if you have any expertise at all in machine learning or probabilistic modeling, and thought that this book might contain some practical advice for designing anomaly detection systems, you'll be sorely disappointed.
Amazon lists this book as being 66 pages, which is only technically true if you count the title page, table of contents, Strata advertisement at the end, and (I'm not making this up) two blank pages. It's a small book with large print, padded with lots and lots of white space and irrelevant photos (like someone holding a magnifying glass over the word "anomaly" on a laptop screen). At some point, apparently, quality control at O'Reilly really went downhill.
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