Advanced Search
MyIDEAS: Login to save this article or follow this journal

Top-10 Data Mining Case Studies

Contents:

Author Info

  • 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):

    Abstract

    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.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.worldscinet.com/cgi-bin/details.cgi?type=pdf&id=pii:S021962201240007X
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: http://www.worldscinet.com/cgi-bin/details.cgi?type=html&id=pii:S021962201240007X
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    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

    as in new window
    Handle: RePEc:wsi:ijitdm:v:11:y:2012:i:02:p:389-400

    Contact details of provider:
    Web page: http://www.worldscinet.com/ijitdm/ijitdm.shtml

    Order Information:
    Email:

    Related research

    Keywords: Data mining; cost-benefit analysis; case study; 68T05; 68U30; 68-01;

    Find related papers by JEL classification:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:wsi:ijitdm:v:11:y:2012:i:02:p:389-400. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Tai Tone Lim).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.