Combining Data Mining and Machine Learning for Effective User Profiling

@inproceedings{Fawcett1996CombiningDM,
  title={Combining Data Mining and Machine Learning for Effective User Profiling},
  author={Tom Fawcett and Foster J. Provost},
  booktitle={KDD},
  year={1996}
}
This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the de&,, ic nrrnm,-,li~h~rl ,,&,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 profiling customer behavior. Specifically, we use a rulelearning program to… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-6 of 6 references

Wireless fraud, now and in the future: A view of the problem and some solutions

  • D. Walters, W. Wilkinson
  • Mobile Phone News 4-7. Yuhas, B. P. 1993. Toll…
  • 1994
1 Excerpt

An introduction to hidden markov models

  • Rabiner. L.R.., B. H. Juang
  • IEEE ASSP Magazine 3(1):4-16.
  • 1986

The analysis of time series

  • C. Chatfield
  • 1984

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