Mining time-changing data streams

Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2001, Pages 97-106

  Hulten, Geoff ^a   Spencer, Laurie ^b   Domingos, Pedro ^a  

^a University of Washington   (United States)

^b Innovation Next   (United States)

Author keywords

Concept drift; Data streams; Decision trees; Hoeffding bounds; Incremental learning; Subsampling

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATABASE SYSTEMS; DECISION THEORY; LEARNING SYSTEMS; TREES (MATHEMATICS);

DATA STREAMS;

DATA MINING;

EID: 0035789299     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/502512.502529     Document Type: Conference Paper

References (29)

1. R. Agrawal and G. Psaila. Active data mining. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pages 3-8, Montréal, Canada, 1995. AAAI Press.
2. N. F. Ayah, A. U. Tansel, and M. E. Arkun. An efficient algorithm to update large itemsets with early pruning. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 287-291, San Diego, CA, 1999. ACM Press.
3. P. L. Bartlett, S. Ben-David, and S. R. Kulkarni. Learning changing concepts by exploiting the structure of change. Machine Learning, 41:153-174, 2000.
4. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, 1984.
5. J. Catlett. Megainduction: Machine Learning on Very Large Databases. PhD thesis, Basser Department of Computer Science, University of Sydney, Sydney, Australia, 1991.
6. S. Chakrabarti, S. Sarawagi, and B. Dom. Mining surprising patterns using temporal description length. In Proceedings of the Twenty-Fourth International Conference on Very Large Data Bases, pages 606-617, New York, NY, 1998. Morgan Kaufmann.
7. D. W.-L. Cheung, J. Han, V. Ng, and C. Y. Wong. Maintenance of discovered association rules in large databases: An incremental updating technique. In Proceedings of the Twelfth International Conference on Data Engineering, pages 106-114, New Orleans, Louisiana, 1996. IEEE Computer Society Press.
8. A. Danyluk, T. Fawcett, and F. Provost, editors. Proceedings of the AAAI-98 Workshop on Predicting the Future: AI Approaches to Time-Series Analysis. AAAI Press, Madison, WI, 1998.
9. P. Domingos and G. Hulten. Mining high-speed data streams. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 71-80, Boston, MA, 2000. ACM Press.
10. T. Fawcett and F. Provost. Activity monitoring: Noticing interesting changes in behavior. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 53-62, San Diego, CA, 1999. ACM Press.
11. V. Ganti, J. Gehrke, and R. Ramakrishnan. DEMON: Mining and monitoring evolving data. In Proceedings of the Sixteenth International Conference on Data Engineering, pages 439-448, San Diego, CA, 2000.
12. J. Gehrke, V. Ganti, R. Ramakrishnan, and W.-L. Loh. BOAT: optimistic decision tree construction. In Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, pages 169-180, Philadelphia, PA, 1999. ACM Press.
13. W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58:13-30, 1963.
14. M. G. Kelly, D. J. Hand, and N. M. Adams. The impact of changing populations on classifier performance. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 367-371, San Diego, CA, 1999. ACM Press.
15. M. Kubat and G. Widmer, editors. Proceedings of the ICML-96 Workshop on Learning in Context-Sensitive Domains. Bari, Italy, 1996.
16. P. M. Long. The complexity of learning according to two models of a drifting environment. Machine Learning, 37:337-354, 1999.
17. M. Mehta, A. Agrawal, and J. Rissanen. SLIQ: A fast scalable classifier for data mining. In Proceedings of the Fifth International Conference on Extending Database Technology, pages 18-32, Avignon, France, 1996. Springer.
18. R. G. Miller, Jr. Simultaneous Statistical Inference. Springer, New York, NY, 2nd edition, 1981.
19. R. Musick, J. Catlett, and S. Russell. Decision theoretic subsampling for induction on large databases. In Proceedings of the Tenth International Conference on Machine Learning, pages 212-219, Amherst, MA, 1993. Morgan Kaufmann.
20. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
21. M. Salganicoff. Density-adaptive learning and forgetting. In Proceedings of the Tenth International Conference on Machine Learning, pages 276-283, Amherst, MA, 1993. Morgan Kaufmann.
22. N. L. Sarda and N. V. Srinivas. An adaptive algorithm for incremental mining of association rules. In Proceedings of the Ninth International Workshop on Database and Expert Systems Applications, pages 240-245, Vienna, Austria, 1998. IEEE.
23. J. C. Schlimmer and R. H. Granger, Jr. Beyond incremental processing: Tracking concept drift. In Proceedings of the Fifth National Conference on Artificial Intelligence, pages 502-507, Philadelphia, PA, 1986. Morgan Kaufmann.
24. J. C. Shafer, R. Agrawal, and M. Mehta. SPRINT: A scalable parallel classifier for data mining. In Proceedings of the Twenty-Second International Conference on Very Large Databases, pages 544-555, Bombay, India, 1996. Morgan Kaufmann.
25. P. Turney. Context-sensitive learning bibliography. Online bibliography, Institute for Information Technology of the National Research Council of Canada, Ottawa, Canada, 1998. http://ai.iit.nrc.ca/-bibliographies/context-sensitive.html.
26. S. Vijayakumar and S. Schaal, editors. Proceedings of the NIPS-2000 Workshop on Real-Time Modeling for Complex Learning Tasks. NIPS Foundation, Breckenridge, Colorado, 2000.
27. G. Widmer and M. Kubat. Learning in the presence of concept drift and hidden contexts. Machine Learning, 23:69-101, 1996.
28. G. Widmer and M. Kubat. Special issue on context sensitivity and concept drift. Machine Learning, 32(2), 1998.
29. A. Wolman, G. Voelker, N. Sharma, N. Cardwell, M. Brown, T. Landray, D. Pinnel, A. Karlin, and H. Levy. Organization-based analysis of Web-object sharing and caching. In Proceedings of the Second USENIX Conference on Internet Technologies and Systems, pages 25-36, Boulder, CO, 1999.