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Top-10 Data Mining Case Studies

  • GABOR MELLI

    ()

    (PredictionWorks Inc., Seattle, WA 98126, USA)

  • XINDONG WU

    ()

    (Department of Computer Science, University of Vermont, Burlington, VT 05405, USA)

  • PAUL BEINAT

    ()

    (NeuronWorks International, Hurtsville, NSW 2220, Australia)

  • FRANCESCO BONCHI

    ()

    (Yahoo! Research, Barcelona, Spain)

  • LONGBING CAO

    ()

    (University of Technology, Sydney, Australia)

  • RONG DUAN

    ()

    (AT&T Labs, Research, Florham Park, NJ, USA)

  • CHRISTOS FALOUTSOS

    ()

    (Department of Computing Science, Carnegie Mellon University, 5000 Forber Avenue, Pittsburgh, PA 15213, USA)

  • RAYID GHANI

    ()

    (Accenture Technology Labs, 161 N.Clark St, Chicago, IL 60601, USA)

  • BRENDAN KITTS

    ()

    (Lucid Commerce, Seattle, WA 98104, USA)

  • BART GOETHALS

    ()

    (Department of Mathematics and Computer Science, University of Antwerp, Belgium)

  • GEOFF MCLACHLAN

    ()

    (Department of Mathematics, University of Queensland, St. Lucia, Brisbane, Australia)

  • JIAN PEI

    ()

    (School of Computing Science, Simon Fraser University, Canada)

  • ASHOK SRIVASTAVA

    ()

    (NASA, USA)

  • OSMAR ZAÏANE

    ()

    (Department of Computing Science, University of Alberta, Alberta, Canada T6G 2E8, Canada)

Registered author(s):

    We report on the panel discussion held at the ICDM'10 conference on the top 10 data mining case studies in order to provide a snapshot of where and how data mining techniques have made significant real-world impact. The tasks covered by 10 case studies range from the detection of anomalies such as cancer, fraud, and system failures to the optimization of organizational operations, and include the automated extraction of information from unstructured sources. From the 10 cases we find that supervised methods prevail while unsupervised techniques play a supporting role. Further, significant domain knowledge is generally required to achieve a completed solution. Finally, we find that successful applications are more commonly associated with continual improvement rather than by single "aha moments" of knowledge ("nugget") discovery.

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    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal International Journal of Information Technology and Decision Making.

    Volume (Year): 11 (2012)
    Issue (Month): 02 ()
    Pages: 389-400

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    Handle: RePEc:wsi:ijitdm:v:11:y:2012:i:02:p:389-400
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