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Abstract: . One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies.... (Update)
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BibTeX entry: (Update)
T. Fawcett and F. Provost. (1997) Adaptive fraud detection. Data Mining and Knowledge Discovery, 1(3). Available: http://www.croftj.net/~fawcett/ DMKD-97.ps.gz. http://citeseer.ist.psu.edu/fawcett97adaptive.html More
@article{ fawcett97adaptive,
author = "Tom Fawcett and Foster J. Provost",
title = "Adaptive Fraud Detection",
journal = "Data Mining and Knowledge Discovery",
volume = "1",
number = "3",
pages = "291-316",
year = "1997",
url = "citeseer.ist.psu.edu/fawcett97adaptive.html" }
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