Using Spark for Anomaly (Fraud) Detection
Anomaly detection is a method used to detect outliers in a dataset and take some action. Example use cases can be detection of fraud in financial transactions, monitoring machines in a large server network, or finding faulty products in manufacturing.
This blog post explains the fundamentals of this Machine Learning algorithm and applies the logic on the Spark framework, in order to allow for large scale data processing:
Running on Spark
Run the tests:
Download Spark (1.6.1) and put it in your Path from here: http://spark.apache.org/downloads.html
Run the project locally. From project root:
spark-submit --class "MainRun" --master local target/scala-2.10/anomaly-detection_2.10-1.0.jar