Practical Machine Learning: A New Look at Anomaly Detection
Publisher: O'Reilly Media
Final Release Date: July 2014
Pages: 66

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

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oreillyPractical Machine Learning: A New Look at Anomaly Detection
 
3.3

(based on 3 reviews)

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        (1 of 1 customers found this review helpful)

         
        2.0

        More like a "glance" and not a "look"

        By Software Engineer / Researcher

        from Southern, NH

        About Me Designer, Developer

        Pros

        • None

        Cons

        • Waste Of Time

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          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.

           
          4.0

          Good Overview

          By Maurice Castro

          from Australia

          About Me Developer, Sys Admin

          Verified Buyer

          Pros

          • Concise

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            • Novice
            • Student

            Comments about oreilly Practical Machine Learning: A New Look at Anomaly Detection:

            A quick introduction to the subject. The mathematics is presented in an accessible way and separated from the intuition making the book useful to both those who want and don't want the mathematics.

            Linkages to practical implementations exist but are pointers to materials not detailed how tos.

            (0 of 1 customers found this review helpful)

             
            4.0

            Useful overview

            By tshear

            from Novi, MI

            About Me Developer

            Verified Buyer

            Pros

            • Concise
            • Easy to understand

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              • Intermediate

              Comments about oreilly Practical Machine Learning: A New Look at Anomaly Detection:

              As a neophyte, this gave me an overview of what to explore further.

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