StreamMiner

Proceedings 2004 VLDB Conference: The 30th International Conference on Very Large Databases (VLDB)

Volumn , Issue , 2004, Pages 1257-1260

  Fan, Wei ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85136029579     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-012088469-8.50121-2     Document Type: Chapter

References (13)

1. B. Babcock, S. Babu, M. Datar, R. Motawani and J. Widom (2002) Models and issues in data stream systems. ACM Symposium on Principles of Database Systems (PODS)
2. S. Babu and J. Widom (2001) Continuous queries over data streams. SIGMOD Record 30 109-120.
3. Y. Chen, G. Dong, J. Han, B.W. Wah and J. Wang (2002) Multi-dimensional regression analysis of time-series data streams. Proc. of Very Large Database (VLDB) Hongkong, China
4. W. Fan (August 2004) Systematic data selection to mine concept-drifting data streams. Proceedings of 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'2004) Seattle, Washington, USA
5. W. Fan (July 2004) On the optimality of probability estimation by random decision trees. Proceedings of the Nineteenth National Conference on, Artificial Intelligence (AAAI'2004) San Jose, California, USA
6. W. Fan, Y. an Huang, H. Wang and P.S. Yu (April 2004) Active mining of data streams. Proceedings of 2004 SIAM International Conference on Data Mining 457-461.
7. W. Fan, H. Wang, P.S. Yu and S. Ma (2003) Is random model better? on its accuracy and efficiency. Proceedings of Third IEEE International Conference on Data Mining (ICDM'2003)
8. L. Gao and X. Wang (2002) Continually evaluating similarity-based pattern queries on a streaming time series. Int'l Conf. Management of Data (SIGMOD) Madison, Wisconsin
9. M. Greenwald and S. Khanna (2001) Space-efficient online computation of quantile summaries. Int'l Conf. Management of Data (SIGMOD) Santa Barbara, CA 58-66.
10. S. Guha, N. Milshra, R. Motwani and L. O'Callaghan (2000) Clustering data streams. IEEE Symposium on Foundations of Computer Science (FOCS) 359-366.
11. G. Hulten, L. Spencer and P. Domingos (2001) Mining time-changing data streams. Int'l Conf. on Knowledge Discovery and Data Mining (SIGKDD) San Francisco, CA: ACM Press 97-106.
12. W.N. Street and Y. Kim (2001) A streaming ensemble algorithm (SEA) for large-scale classification. Int'l Conf. on Knowledge Discovery and Data Mining (SIGKDD)
13. H. Wang, W. Fan, P. Yu and J. Han (2003) Mining concept-drifting data streams with ensemble classifiers. Proceedings of ACM SIGKDD International Conference on knowledge discovery and data mining (SIGKDD2003) 226-235.