Numerous big data methods have been unable to eradicate fraud completely. It’s important to score customer transactions to prevent the takeover, but crucial information about where the accounts were intercepted may be lurking in plain sight, completely overlooked.
In just a few simple steps, you can analyze your data to find the source of compromise. Join this session of Free Code Fridays where you'll get to hear from Joe Blue, Data Scientist at MapR. You'll learn how to use Apache Drill to analyze massive amounts of semi-structured transactions in seconds using the Map-Reduce model, and shut down a breach before it does real damage.
Depends on size and overlap. Significance is measured in overlap beyond expected.
1 vs. 2. – both rare items so wouldn’t expect much overlap, but we see total (slightly askew to show both circles)
3 vs. 4 – popular items, so expect higher number of overlap
Can distribute these calculations (map-reduce, Mahout, Spark, etc.)