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Business applications of data mining

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Published:01 August 2002Publication History
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Abstract

They help identify and predict individual, as well as aggregate, behavior, as illustrated by four application domains: direct mail, retail, automobile insurance, and health care.

References

  1. Apte, C., Bibelnieks, E., Natarajan, R., Pednault, E., Tipu, F., Campbell, D., and Nelson, B. Segmentation-based modeling for advanced targeted marketing. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001) (San Francisco, Aug. 26--29). ACM Press, New York, 2001, 408--413. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Apte, E., Grossman, E., Pednault, E., Rosen, B., Tipu, F., and White, B. Probabilistic estimation-based data mining for discovering insurance risks. IEEE Intelli. Syst. 14, 6 (Nov./Dec. 1999), 49--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Cadez, I., Smyth, P., and Mannila, H. Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001) (San Francisco, Aug. 26--29). ACM Press, New York, 2001, 37--46. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Hsu, W., Lee, M., Liu, B., and Ling, T. Exploration mining in diabetic patient databases: Findings and conclusions. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000) (Boston, Aug. 20--23). ACM Press, New York, 2000, 430--436. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Liu, B., Hu, M., and Hsu, W. Multi-level organization and summarization of the discovered rules. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000) (Boston, Aug. 20--23). ACM Press, New York, 2000, 208--217. Google ScholarGoogle ScholarDigital LibraryDigital Library

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              cover image Communications of the ACM
              Communications of the ACM  Volume 45, Issue 8
              Evolving data mining into solutions for insights
              August 2002
              96 pages
              ISSN:0001-0782
              EISSN:1557-7317
              DOI:10.1145/545151
              Issue’s Table of Contents

              Copyright © 2002 ACM

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              Publication History

              • Published: 1 August 2002

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