Combining data mining and machine learning for effective user profiling

Published 2 August 1996

Tom Fawcett, Foster Provost

Citations174

TL;DR

This paper combines data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior, and uses a rule-learning program to uncover indicators of fraudulent behavior from a large database of cellular calls.

Abstract

This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the de-&amp;,, ic nrrnm,-,li~h~rl,,&amp;,a n.am.L ~ nf mn.-h;na lm..~:~ ~ e-..-...--..*.*yYYA’“.. UY.“b Y UISLUY “I III-Yllr IxuIY11~ methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on pro-filing customer behavior. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned meth-ods for detecting fraud.

Keywords

Computer Science

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