Practical Machine Learning: A New Look at Anomaly Detection Ted Dunning, Ellen Friedman

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Autorzy:: Ted Dunning, Ellen Friedman
Wydawnictwo:: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 66
Dostępne formaty:: ePub

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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

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Szczegóły książki

ISBN Ebooka:: 978-14-919-1417-5, 9781491914175
Data wydania ebooka :: 2014-07-21 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: angielski
Rozmiar pliku ePub:: 3.7MB
Rozmiar pliku Mobi:: 7MB

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Kategorie:
Big Data » Analiza danych
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Spis treści książki

Practical Machine Learning
1. Looking Toward the Future
2. The Shape of Anomaly Detection
- Finding Normal
  - If you enjoy math, read this description of a probabilistic model of normal
- Human Insight Helps
- Finding Anomalies
  - Once again, if you like math, this description of anomalies is for you
  - Take-Home Lesson: Key Steps in Anomaly Detection
- A Simple Approach: Threshold Models
3. Using t-Digest for Threshold Automation
- The Philosophy Behind Setting the Threshold
- Using t-Digest for Accurate Calculation of Extreme Quantiles
- Issues with Simple Thresholds
4. More Complex, Adaptive Models
- Windows and Clusters
- Matches with the Windowed Reconstruction: Normal Function
- Mismatches with the Windowed Reconstruction: Anomalous Function
- A Powerful But Simple Technique
- Looking Toward Modeling More Problematic Inputs
5. Anomalies in Sporadic Events
- Counts Dont Work Well
- Arrival Times Are the Key
  - And Now with the Math
- Event Rate in a Worked Example: Website Traffic Prediction
- Extreme Seasonality Effects
6. No Phishing Allowed!
- The Phishing Attack
- The No-Phishing-Allowed Anomaly Detector
- How the Model Works
- Putting It All Together
7. Anomaly Detection for the Future
A. Additional Resources
- GitHub
- Apache Mahout Open Source Project
- Additional Publications
About the Authors
Colophon
Copyright