R. Grossman, S. Kasif, R. Moore, D. Rocke, and J. Ullman. Data Mining Research: Opportunities and Challenges, A report of three NSF workshops on Mining Large, Massive, and Distributed Data, January 1998. Home/Search Document Not in Database Summary Related Articles Check |
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Collusion Program' in The U.S. Crop Insurance Applied Data.. - Bertis Little Walter (Correct)
....areas where claims exceed the norm. The insurance fh m investigated and confumed that a physician in an area was submitting false bills. As a result, the doctor was forced to pay restitution and fines. Data mining saved this health insurance company as much as 4 million. Grossman et al. [2] have indicated that fraud detection is one of the areas where data mining is considered a successful tool. He et al. [3] showed how data mining through neural networks could be used to detect medical fraud. Several recent publications have documented the usefulness of data mining techniques for ....
Grossman, R., S. Kasif, R. Moore, D. Rocke, and J. Ullman. 1999. Data Mining Research: Opportunities and Challenge. A report of three NSF workshops on Mining Large, Massive and Distributed Data. In: http ://www.ncdm.uic.edu/ dmr-v8-4- 5 2.htm .
Decomposition in Data Mining: An Industrial Case Study - Kusiak (2000) (1 citation) (Correct)
....sources of papers on distributed learning is the edited volume by Zaki and Ho [22] Several contributors to this book discussed ways of leveraging parallel and distributed techniques in knowledge discovery, such as data cleaning and preprocessing, transformation, and learning. Grossman et al. [23] outlined fundamental challenges for mining large sale databases, with one of them being the need to develop distributed data mining algorithms. Guo and Sutiwaraphun [24] described a meta learning concept named Knowledge Probing to distributed data mining. In Knowledge Probing, supervised learning ....
R. Grossman, S. Kasif, R. Moore, D. Rocke, and J. Ullman, "Data mining research: Opportunities and challenges. Report of three NSF workshops on mining large, massive, and distributed data," Tech. Rep., http://www.ncdm.uic.edu/M3D-final-report.htm, 1999.
Adaptive Performance Prediction for Distributed.. - Faerman, Su, Wolski.. (1999) (21 citations) (Correct)
....14] are becoming an increasingly important platform for applications which perform calculations over large datasets. Such applications include image acquisition and processing calculations, digital library searches, high performance massive data assimilation, distributed data mining and others [11, 17, 15, 10, 1, 24, 3]. Aggregating distributed resources presents the opportunity to employ or acquire data from very large datasets which are too large to be stored at a single site. This research was supported in part by NASA Graduate Student Research Grant #NGT 2 52251, Department of Defense Modernization ....
R. Grossman, S. Kasif, R. Moore, D. Rocke, and J. Ullman. Data Mining Research: Opportunities and Challenges - A Report of three NSF Workshops on Mining Large, Massive, and Distributed Data, 1998. Available at http://www.ncdm.uic.edu/M3D-final-report.htm.
Adaptive Performance Prediction for Distributed.. - Faerman, Su, Wolski.. (1999) (21 citations) (Correct)
....[18] are becoming an increasingly important platform for applications which perform computations over large datasets. Such applications include image acquisition and processing computations, digital library searches, high performance massive data assimilation, distributed data mining and others [3, 6, 16, 17, 20, 25, 35]. Aggregating distributed resources on the grid presents the opportunity to employ or acquire data from massive datasets which are too large to be stored at a single site. This research was supported in part by NASA Graduate Student Research Grant #NGT 2 52251, Department of Defense ....
R. Grossman, S. Kasif, R. Moore, D. Rocke, and J. Ullman. Data Mining Research: Opportunities and Challenges - A Report of three NSF Workshops on Mining Large, Massive, and Distributed Data, 1998. Available at http://www.ncdm.uic.edu/M3D-finalreport. htm.
Intrusion Detection Systems Using Decision Trees and.. - Sandhya.. (Correct)
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R. Grossman, S. Kasif, R. Moore, D. Rocke, and J. Ullman. Data Mining Research: Opportunities and Challenges, A report of three NSF workshops on Mining Large, Massive, and Distributed Data, January 1998.
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