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Emerging scientific applications in data mining
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Communications of the ACM archive
Volume 45 ,  Issue 8  (August 2002) table of contents
Evolving data mining into solutions for insights
SPECIAL ISSUE: Evolving data mining into solutions for insights table of contents
Pages: 54 - 58  
Year of Publication: 2002
ISSN:0001-0782
Authors
Jiawei Han  Department of Computer Science in the University of Illinois at Urbana-Champaign.
Russ B. Altman  Department of Genetics, Stanford Medical Informatics, Stanford University, Stanford, CA.
Vipin Kumar  Computer Science Department at the University of Minnesota, Minneapolis.
Heikki Mannila  Department of Computer Science at the University of Helsinki, Finland.
Daryl Pregibon  AT&T Labs, Research, Florham Park, NJ.
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 28,   Downloads (12 Months): 276,   Citation Count: 5
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ABSTRACT

Automated, scalable systems would reveal and help exploit the deeper meanings in scientific data, especially in biomedical engineering, telecommunications, geospatial exploration, and climate and Earth ecosystem modeling.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Kumar, V., Steinbach, M., Tan, P., Klooster, S., Potter, C., and Torregrosa, A. Mining scientific data: Discovery of patterns in the global climate system. In Proceedings of the Joint Statistical Meetings (Athens, GA, Aug. 5--9). American Statistical Association, Alexandria, VA, 2001.
 
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Steinbach, M., Tan, P., Kumar, V., Klooster, S., and Potter, C. Data mining for the discovery of ocean climate indices. In Proceedings of the 5th Workshop on Scientific Data Mining (SDM 2002) (Arlington, VA, Apr. 13). Society of Industrial and Applied Mathematics, 2002, 7--16.


Collaborative Colleagues:
Jiawei Han: colleagues
Russ B. Altman: colleagues
Vipin Kumar: colleagues
Heikki Mannila: colleagues
Daryl Pregibon: colleagues