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MAIDS: mining alarming incidents from data streams
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Proceedings of the 2004 ACM SIGMOD international conference on Management of data table of contents
Paris, France
DEMONSTRATION SESSION: Data mining table of contents
Pages: 919 - 920  
Year of Publication: 2004
ISBN:1-58113-859-8
Authors
Y. Dora Cai  University of Illinois at Urbana-Champaign
David Clutter  University of Illinois at Urbana-Champaign
Greg Pape  University of Illinois at Urbana-Champaign
Jiawei Han  University of Illinois at Urbana-Champaign
Michael Welge  University of Illinois at Urbana-Champaign
Loretta Auvil  University of Illinois at Urbana-Champaign
Sponsor
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 97,   Citation Count: 6
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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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C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for clustering evolving data streams. VLDB'03.
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Y. Chen, G. Dong, J. Han, B. W. Wah, and J. Wang. Multi-dimensional regression analysis of time-series data streams. VLDB'02.
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T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, 2001.
 
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M. Welge, et al. Data to knowledge (D2K), A rapid application development environment for knowledge discovery in databases. NCSA Technical Report, 2003.
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Collaborative Colleagues:
Y. Dora Cai: colleagues
David Clutter: colleagues
Greg Pape: colleagues
Jiawei Han: colleagues
Michael Welge: colleagues
Loretta Auvil: colleagues