An efficient decision tree classification method based on extended hash table for data streams mining

Proceedings - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008

Volumn 5, Issue , 2008, Pages 313-317

  Ouyang, Zhenzheng ^a   Wu, Quanyuan ^a   Wang, Tao ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DATA HANDLING; DATA MINING; DECISION TABLES; DECISION THEORY; DECISION TREES; FUZZY LOGIC; FUZZY SYSTEMS; MATHEMATICAL MODELS;

ATTRIBUTE VALUES; CLASSIFICATION METHODS; CONCEPT DRIFTS; CONTINUOUS ATTRIBUTES; DATA STREAMS; EXTENDED VERSIONS; HASH TABLES; HOEFFDING INEQUALITIES; PROBABILISTIC BOUNDS; REAL TIMES;

TREES (MATHEMATICS);

EID: 58149134494     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FSKD.2008.481     Document Type: Conference Paper

References (21)

1. B. Babcock, S. Babu, M. Datar, R. Motawani, and J. Widom. Models and Issues in Data Stream Systems. In Proceedings of 21st ACM Symposium on Principles of Database Systems 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(2000) 71-80.
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, Avignon, France(1996)18-32.
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 Time-Changing 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(2002)129-147.
9. S. Muthukrishnan. Data streams: Algorithms and Applications. Proceedings of the fourteenth annual ACMSIAM symposium on discrete algorithms (2003).
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, Washington DC, USA. SIGKDD(2003),
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(2004).
13. M.M. Gaber, A. Zaslavskey and S. Krishnaswamy. Mining Data Streams: a Review. SIGMOD Record, Vol. 34, No. 2, June 2005.
14. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA(1993).
15. W. Hoeffding. Probability Inequalities for Sums of Bounded Random Variables. Journal of the American Statistical Association, 58(1963)13-30.
16. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J.Stone. Classification and Regression Trees. Wadsworth, Belmont, CA(1984).
17. 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 (1994).
18. O.Maimon, M.Last. Knowledge Discovery and Data Mining - the Info-Fuzzy Network(IFN) Methodology. Kluwer Academic Publishers(2000).
19. U.M Fayyad, K.B.Irani. On the Handling of Continuous-valued Attributes in Decision Tree Generation. Machine Learning, 8(1992) 87-102.
20. Tao Wang, Zhoujun Li, Yuejin Yan and Huowang Chen. An Efficient Classification System Based on Binary Search Trees for Data Streams Mining. ICONS(2007).
21. Peng Y.H., Flach P.A.. Soft Discretization to Enhance the Continuous Decision Tree Induction. Proceedings of ECML/PKDD-2001 Workshop IDDM-2001, Freiburg, Germany.