Brief Application Description; Visual Data Mining: Recognizing Telephone Calling Fraud

  • Authors:
  • Kenneth C. Cox;Stephen G. Eick;Graham J. Wills;Ronald J. Brachman

  • Affiliations:
  • Bell Laboratories/Lucent Technologies, Room 1G-351, 1000 East Warrenville Road, Naperville, IL 60566. E-mail: kcc@research.bell-labs.com, eick@research.bell-labs.com, gwills@research.bell-labs.com;Bell Laboratories/Lucent Technologies, Room 1G-351, 1000 East Warrenville Road, Naperville, IL 60566. E-mail: kcc@research.bell-labs.com, eick@research.bell-labs.com, gwills@research.bell-labs.com;Bell Laboratories/Lucent Technologies, Room 1G-351, 1000 East Warrenville Road, Naperville, IL 60566. E-mail: kcc@research.bell-labs.com, eick@research.bell-labs.com, gwills@research.bell-labs.com;AT&T Laboratories

  • Venue:
  • Data Mining and Knowledge Discovery
  • Year:
  • 1997

Quantified Score

Hi-index 0.01

Visualization

Abstract

Human pattern recognition skills are remarkable and in manysituations far exceed the ability of automated mining algorithms. Bybuilding domain-specific interfaces that present informationvisually, we can combine human detection with machines‘ far greatercomputational capacity. We illustrate our ideas by describing asuite of visual interfaces we built for telephone fraud detection.