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Characteristic rule induction algorithm for data mining

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Research and Development in Knowledge Discovery and Data Mining (PAKDD 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1394))

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References

  1. C. J. Matheus, et.al.: Systems for Knowledge Discovery in Databases, IEEE Transactions on Knowledge and Data Engineering, Vol.5, No.6, pp.903–913 (1993)

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  2. G. Piatetsky-Shapiro: Discovery, Analysis, and Presentation of Strong Rules, Knowledge Discovery in Databases, pp.229–248, AAAI Press(1991)

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  3. Y. Satoh, et.al.: A Data Mining Technique for Time Series Data, to be presented at the Ninth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE'96) (1996)

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Author information

Authors and Affiliations

  1. Systems Development Laboratory, Hitachi, Ltd., 1099 Ohzenji, 215, Asao-ku Kawasaki, Japan

    Akira Maeda & Hideyuki Maki

  2. Device Development Center, Hitachi, Ltd., 2326 Imai, 198, Ohme-city, Tokyo, Japan

    Hiroyuki Akimori

Authors
  1. Akira Maeda
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  2. Hideyuki Maki
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  3. Hiroyuki Akimori
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Editor information

Editors and Affiliations

  1. School of Computer Science and Software Engineering, Monash university, 900 Dandenong Road, Caulfield East, Victoria, 3145, Australia

    Xindong Wu

  2. Department of Computer Science, The University of Melbourne, Parkville, Victoria, 3052, Australia

    Ramamohanarao Kotagiri

  3. School of Computer Science and Engineering, Monash university, Clayton, Victoria, 3168, Australia

    Kevin B. Korb

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© 1998 Springer-Verlag Berlin Heidelberg

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Maeda, A., Maki, H., Akimori, H. (1998). Characteristic rule induction algorithm for data mining. In: Wu, X., Kotagiri, R., Korb, K.B. (eds) Research and Development in Knowledge Discovery and Data Mining. PAKDD 1998. Lecture Notes in Computer Science, vol 1394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64383-4_42

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  • DOI: https://doi.org/10.1007/3-540-64383-4_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64383-8

  • Online ISBN: 978-3-540-69768-8

  • eBook Packages: Springer Book Archive

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