Top-10 data mining case studies

Melli, G; Wu, X; Beinat, P; Bonchi, F; Cao, L; Duan, R; Faloutsos, C; Ghani, R; Kitts, B; Goethals, B; Mclachlan, G; Pei, J; Srivastava, A; Zaïane, O

Top-10 data mining case studies

Melli, G Wu, X Beinat, P Bonchi, F Cao, L

Duan, R Faloutsos, C Ghani, R Kitts, B Goethals, B Mclachlan, G Pei, J Srivastava, A Zaïane, O

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Publication Type:: Journal Article
Citation:: International Journal of Information Technology and Decision Making, 2012, 11 (2), pp. 389 - 400
Issue Date:: 2012-03-01

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Field	Value	Language
dc.contributor.author	Melli, G	en_US
dc.contributor.author	Wu, X	en_US
dc.contributor.author	Beinat, P	en_US
dc.contributor.author	Bonchi, F	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Duan, R	en_US
dc.contributor.author	Faloutsos, C	en_US
dc.contributor.author	Ghani, R	en_US
dc.contributor.author	Kitts, B	en_US
dc.contributor.author	Goethals, B	en_US
dc.contributor.author	Mclachlan, G	en_US
dc.contributor.author	Pei, J	en_US
dc.contributor.author	Srivastava, A	en_US
dc.contributor.author	Zaïane, O	en_US
dc.date.issued	2012-03-01	en_US
dc.identifier.citation	International Journal of Information Technology and Decision Making, 2012, 11 (2), pp. 389 - 400	en_US
dc.identifier.issn	0219-6220	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22297
dc.description.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. © 2012 World Scientific Publishing Company.	en_US
dc.relation.ispartof	International Journal of Information Technology and Decision Making	en_US
dc.relation.isbasedon	10.1142/S021962201240007X	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Top-10 data mining case studies	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	11	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1503 Business and Management	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

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. © 2012 World Scientific Publishing Company.

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http://hdl.handle.net/10453/22297