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Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets

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Knowledge Discovery and Data Mining. Current Issues and New Applications (PAKDD 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1805))

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Abstract

This paper presents a validation, with two common medical data sets, of exception-rule discovery based on a hypothesis-driven approach. The analysis confirmed the effectiveness of the approach in discovering valid, novel and surprising knowledge.

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References

  1. E. Suzuki and M. Shimura: Exceptional Knowledge Discovery in Databases Based on Information Theory, Proc. Second Int’l Conf. Knowledge Discovery and Data Mining (KDD-96), AAAI Press, Menlo Park, Calif., pp. 275–278 (1996).

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  2. E. Suzuki: “Autonomous Discovery of Reliable Exception Rules”, Proc. Third Int’l Conf. Knowledge Discovery and Data Mining (KDD-97), AAAI Press, Menlo Park, Calif., pp. 259–262 (1997).

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  3. E. Suzuki and Y. Kodratoff: “Discovery of Surprising Exception Rules based on Intensity of Implication”, Principles of Data Mining and Knowledge Discovery (PKDD’98), LNAI 1510, Springer, Berlin, pp. 10–18 (1998).

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  4. E. Suzuki: “Scheduled Discovery of Exception Rules”, Discovery Science (DS’99), LNAI 1721, Springer, Berlin, pp. 184–195 (1999).

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  5. S. Tsumoto et al.: “Comparison of Data Mining Methods using Common Medical Datasets”, ISM Symposium: Data Mining and Knowledge Discovery in Data Science, The Inst. of Statistical Math., Tokyo, pp. 63–72 (1999).

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Author information

Authors and Affiliations

  1. Division of Electrical and Computer Engineering, Faculty of Engineering, Yokohama National University, Yokohama

    Einoshin Suzuki

  2. Department of Medical Informatics, Shimane Medical University, School of Medicine, Japan

    Shusaku Tsumoto

Authors
  1. Einoshin Suzuki
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  2. Shusaku Tsumoto
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Editor information

Editors and Affiliations

  1. Graduate School of Systems Management, Universiy of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan

    Takao Terano

  2. Department of Computer Science and Engineering, Arizona State University, P.O. Box 875 406, Tempe, AZ, 85287-5406

    Huan Liu

  3. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan ROC

    Arbee L. P. Chen

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© 2000 Springer-Verlag Berlin Heidelberg

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Suzuki, E., Tsumoto, S. (2000). Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_26

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  • DOI: https://doi.org/10.1007/3-540-45571-X_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

  • eBook Packages: Springer Book Archive

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