Practical Machine Learning: A New Look at Anomaly Detection

Nonfiction, Computers, Database Management, Data Processing, Programming
Cover of the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning, Ellen Friedman, O'Reilly Media
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Author: Ted Dunning, Ellen Friedman ISBN: 9781491914175
Publisher: O'Reilly Media Publication: July 21, 2014
Imprint: O'Reilly Media Language: English
Author: Ted Dunning, Ellen Friedman
ISBN: 9781491914175
Publisher: O'Reilly Media
Publication: July 21, 2014
Imprint: O'Reilly Media
Language: English

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
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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