Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. In this O’Reilly report, two committers of the Apache Mahout project use 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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Practical Machine Learning:
A New Look At Anomaly Detection
by Ted Dunning and Ellen Friedman