An efficient classification system based on binary search trees for data streams mining

2nd International Conference on Systems, ICONS 2007

Volumn , Issue , 2007, Pages 15-20

  Wang, Tao ^a   Li, Zhoujun ^b   Yan, Yuejin ^a   Chen, Huowang ^a   Yu, Jinshan ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

^b BEIHANG UNIVERSITY   (China)

Author keywords

Binary search tree; Data streams; Numerical attributes; VFDT

Indexed keywords

BINARY TREES; DATA MINING; DATA STREAMS; DECISION TREES; TREES (MATHEMATICS);

BINARY SEARCH TREES; CLASSIFICATION SYSTEM; DECISION TREE CLASSIFIERS; DECISION TREE CONSTRUCTION; HOEFFDING INEQUALITY; NUMERICAL ATTRIBUTES; PROBABILISTIC BOUNDS; VFDT;

SEARCH ENGINES;

EID: 46349094540     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICONS.2007.12     Document Type: Conference Paper

References (31)

1. B. Babcock, S. Babu, M. Datar, R. Motawani, and J. Widom. Models and Issues in Data Stream Systems. In PODS, 2002.
2. P. Domingos and G. Hulten. Mining High-Speed Data Streams. In Proceedings of the Association for Computing Machinery Sixth International Conference on Knowledge Discovery and Data Mining, pages 71-80, 2000.
3. 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.
4. Wei Fan. StreamMiner: A Classifier Ensemble-based Engine to Mine Concept Drifting Data Streams. VLDB'2004.
5. J. Gama, R. Rocha and P. Medas. Accurate Decision Trees for Mining High-Speed Data Streams. Proceedings of the Ninth International Conference on Knowledge Discovery and Data Mining, Edited by P.Domingos and C. Faloutsos, ACM Press, 2003.
6. G. Hulten, L. Spencer, and P. Domingos. Mining TimeChanging Data Streams. ACM SIGKDD 2001.
7. R. Jin and G. Agrawal. Efficient Decision Tree Construction on Streaming Data. In proceedings of ACM SIGKDD 2003.
8. M. Last. Online Classification of Nonstationary Data Streams. Intelligent Data Analysis, Vol. 6, No. 2, pp. 129-147, 2002.
9. S. Muthukrishnan (2003). Data streams: Algorithms and Applications. Proceedings of the fourteenth annual ACM-SIAM symposium on discrete algorithms.
10. H. Wang, W. Fan, P. Yu and J. Han. Mining Concept-Drifting Data Streams using Ensemble Classifiers. In the 9th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Aug. 2003, Washington DC, USA.
11. A. Arasu, B. Babcock. S. Babu, M. Datar, K. Ito, I. Nishizawa, J. Rosenstein, and J. Widom. STREAM: The Stanford Stream Data Manager Demonstration Description Short Overview of System Status and Plans. In Proc. of the ACM Intl Conf. on Management of Data (SIGMOD 2003), June 2003.
12. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. On Demand Classification of Data Streams. Proc. 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04), Seattle, WA, Aug. 2004.
13. M.Guetova, Holldobter, and H.-P Storr. Incremental Fuzzy Decision Trees. 25th German conference on Artificial Intelligence (KI2002)
14. Ben-David, S, Johannes Gehrke and Daniel Kifer. Detecting Change in Data Streams. In Proceedings of VLDB 2004.
15. C. Aggarwal. A Framework for Diagnosing Changes in Evolving Data Streams. Proceedings of the ACM SIGMOD Conference, 2003.
16. M.M. Gaber, A. Zaslavskey and S. Krishnaswamy. Mining Data Streams: a Review. SIGMOD Record, Vol. 34, No. 2, June 2005.
17. Cezary, Z.Janikow. Fuzzy Decision Trees: Issues and Methods. IEEE Transactions on Systems, Man, and Cybernetics, 28(1): 1-14, 1998
18. P. E. Utgoff, (1989b). Incremental Induction of Decision Trees. Machine Learning, Vol. 4, Issue 2, pp. 161-186, 1989.
19. Q.H Xie. An Efficient Approach for Mining ConceptDrifting Data Streams. Master Thesis.
20. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
21. W. Hoeffding. Probability Inequalities for Sums of Bounded Random Variables. Journal of the American Statistical Association, 58:13{30, 1963}.
22. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J.Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, 1984.
23. O. Maron and A. Moore. Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation. In J.D.Cowan, G.Tesauro, and J. Alspector, editors, Advances in Neural Information Processing System.
24. M.G.Kelly, D.J.Hand, and N.M.Adams. The Impact of Changing Populations on Classifier Performance. Proc. of KDD-99, pages 367-371, 1999.
25. M.Black, R.J.Hickey. Maintaining the Performance of a Learned Classifier under Concept Drift. Intelligent Data Analysis, No.3 pp.453-474, 1999.
26. O.Maimon, M.Last. Knowledge Discovery and Data Mining, the Info-Fuzzy Network(IFN) Methodology. Kluwer Academic Publishers, 2000.
27. U.M Fayyad, K.B.Irani, On the Handling of Numerical-valued Attributes in Decision Tree Generationon Learning. Machine Learning, 9, 87-102.
28. U.M Fayyad, K.B.Irani, Multi-interval Discretization of Numerical-valued Attributes for Classification Learning. In Proc of the 13th IJCAI, pages 1022-1027, Chambery, France, 1993.
29. J.Gehrke, R.Ramarkrishnan, and V.Ganti. RaiW.Lohnforest-A Framework for Fast Decision Tree Construction of Large Datasets. In VLDB, 1998.
30. J.Gehrke, V.Ganti, R.Ramarkrishnan, and W.Loh. Boat-optimistic Decision Tree Construction. In proc. of the ACM SIGMOD conference on management of data, June 1999.
31. J.Shafer,R.Agrawal,and M.Mehta. SPRINT: A Scalable Parallel Classifier for Data Mining. In proceedings of the 22nd international conference on VLDB, pages 544-555,September 1996.