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  1. Data Mining and Knowledge Discovery
  2. Data Mining and Knowledge Discovery : Volume 1
  3. Data Mining and Knowledge Discovery : Volume 1, Issue 3, September 1997
  4. Adaptive Fraud Detection
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Data Mining and Knowledge Discovery : Volume 31
Data Mining and Knowledge Discovery : Volume 30
Data Mining and Knowledge Discovery : Volume 29
Data Mining and Knowledge Discovery : Volume 28
Data Mining and Knowledge Discovery : Volume 27
Data Mining and Knowledge Discovery : Volume 26
Data Mining and Knowledge Discovery : Volume 25
Data Mining and Knowledge Discovery : Volume 24
Data Mining and Knowledge Discovery : Volume 23
Data Mining and Knowledge Discovery : Volume 22
Data Mining and Knowledge Discovery : Volume 21
Data Mining and Knowledge Discovery : Volume 20
Data Mining and Knowledge Discovery : Volume 19
Data Mining and Knowledge Discovery : Volume 18
Data Mining and Knowledge Discovery : Volume 17
Data Mining and Knowledge Discovery : Volume 16
Data Mining and Knowledge Discovery : Volume 15
Data Mining and Knowledge Discovery : Volume 14
Data Mining and Knowledge Discovery : Volume 13
Data Mining and Knowledge Discovery : Volume 12
Data Mining and Knowledge Discovery : Volume 11
Data Mining and Knowledge Discovery : Volume 10
Data Mining and Knowledge Discovery : Volume 9
Data Mining and Knowledge Discovery : Volume 8
Data Mining and Knowledge Discovery : Volume 7
Data Mining and Knowledge Discovery : Volume 6
Data Mining and Knowledge Discovery : Volume 5
Data Mining and Knowledge Discovery : Volume 4
Data Mining and Knowledge Discovery : Volume 3
Data Mining and Knowledge Discovery : Volume 2
Data Mining and Knowledge Discovery : Volume 1
Data Mining and Knowledge Discovery : Volume 1, Issue 4, December 1997
Data Mining and Knowledge Discovery : Volume 1, Issue 3, September 1997
Editorial
Levelwise Search and Borders of Theories in Knowledge Discovery
Discovery of Frequent Episodes in Event Sequences
Adaptive Fraud Detection
On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
Data Mining and Knowledge Discovery : Volume 1, Issue 2, June 1997
Data Mining and Knowledge Discovery : Volume 1, Issue 1, March 1997

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Adaptive Fraud Detection

Content Provider Springer Nature Link
Author Fawcett, Tom Provost, Foster
Copyright Year 1997
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. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning fraud based on a database of call records. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments.
Starting Page 291
Ending Page 316
Page Count 26
File Format PDF
ISSN 13845810
Journal Data Mining and Knowledge Discovery
Volume Number 1
Issue Number 3
e-ISSN 1573756X
Language English
Publisher Kluwer Academic Publishers
Publisher Date 1997-01-01
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Data Structures, Cryptology and Information Theory Information Storage and Retrieval Artificial Intelligence (incl. Robotics) Statistics Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences
Content Type Text
Resource Type Article
Subject Computer Networks and Communications Information Systems Computer Science Applications
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