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Mining association rules for estimation and prediction

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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

  • G. Piatestsky-Shapiro.: Discovery, analysis, and presentation of strong rules. Knowledge Discovery in Databases. AAAI/MIT Press (1991)

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  • R.Agrwal and R.Srikant.: Fast algorithms for mining association rules. In Proc. of the 20th VLDB Conference, (1994) 487–499

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  • R.Kimball.: The Data Warehouse Toolkit. Wiley Computer Publishing (1996)

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  • U.Fayyad, G.Piatesky-Shapiro and P.Smyth.: From Data Mining to Knowledge Discovery in Databases. AI MAGAZINE FALL, (1996) 37–54

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  • R.Srikant, Q. Vu and R.Agrwal.: Mining Association Rules with Item Constraints. In Proc. of 3rd Conference on Knowledge Discovery and Data Mining, (1997) 67–73

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Author information

Authors and Affiliations

  1. Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, 567, Ibaraki, Osaka, Japan

    Takashi Washio & Hiroshi Motoda

  2. Fujitsu Kansai Communication Systems Ltd., Japan

    Hiroki Matsuura

Authors
  1. Takashi Washio
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  2. Hiroki Matsuura
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  3. Hiroshi Motoda
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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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Washio, T., Matsuura, H., Motoda, H. (1998). Mining association rules for estimation and prediction. 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_50

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  • DOI: https://doi.org/10.1007/3-540-64383-4_50

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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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