Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams-Reference-Cited by-同舟云学术

Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams

Published:2006-06-29 Issue:3 Volume:13 Page:261-289
ISSN:1384-5810
Container-title:Data Mining and Knowledge Discovery
language:en
Short-container-title:Data Min Knowl Disc

Author:

Yang Ying,Wu Xindong,Zhu Xingquan

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://link.springer.com/content/pdf/10.1007/s10618-006-0050-x.pdf

Reference18 articles.

1. Aggarwal, C.C., Han, J., Wang, J., and Yu, P.S. 2003. A framework for clustering evolving data streams. In Proceedings of the 29th International Conference on Very Large Data Bases, pp. 81–92.

2. Blake, C.L. and Merz, C.J. 2005. UCI repository of machine learning databases [ http://www.ics.uci.edu/~mlearn/milrepository.html ] Department of Information and Computer Science, University of California, Irvine.

3. Ganti, V., Gehrke, J., and Ramakrishnan, R. 2001. Demon: Mining and monitoring evolving data. IEEE Transactions on Knowledge and Data Engineering, 13:50–63.

4. Gehrke, J., Ganti, V., Ramakrishnan, R., and Loh, W.Y. 1999. Boat-optimistic decision tree construction. In Proceedings ACM SIGMOD International Conference on Management of Data, pp. 169–180.

5. Harries, M.B. and Horn, K. 1996. Learning stable concepts in a changing world. In PRICAI Workshops, pp. 106–122.

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