TOP-10 DATA MINING CASE STUDIES

Gabor Melli (), Xindong Wu (), Paul Beinat (), Francesco Bonchi (), Longbing Cao (), Rong Duan (), Christos Faloutsos (), Rayid Ghani (), Brendan Kitts (), Bart Goethals (), Geoff McLachlan (), Jian Pei (), Ashok Srivastava () and Osmar Zaïane ()
Additional contact information
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

International Journal of Information Technology & Decision Making (IJITDM), 2012, vol. 11, issue 02, 389-400

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.

Keywords: Data mining; cost-benefit analysis; case study; 68T05; 68U30; 68-01 (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1142/S021962201240007X

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