Sampling ensembles for frequent patterns

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3613, Issue PART I, 2005, Pages 1197-1206

  Jia, Caiyan ^a,b   Lu, Ruqian ^a,b,c  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b FUDAN UNIVERSITY   (China)

^c INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ERROR ANALYSIS; LEARNING SYSTEMS; MONTE CARLO METHODS; RANDOM PROCESSES;

BIAS-VARIANCE DECOMPOSITION; MONTE CARLO THEORY; RULE MINING; SCALABILITY;

DATA MINING;

EID: 26944433777     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Agrawal, R., Srikant, E.: Fast Algorithms for Mining Association Rules. In VLDB'94, Santiago, Chile (1994) 487-499
2. Zaki, M. J., Hsiao, C.-J.: CHARM: An Efficient Algorithm for Closed Association Rule Mining, Technical Report 99-10, Computer Science Dept., Rensselaer Polytechnic Institute (1999)
3. Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns Without Candidate Generation. In SIGMOD'00, Dallas, TX (2000) 1-12
4. Chen, B., Haas, P., Scheuermann, P,: A New Two-phase Sampling Based Algorithms for Discovery Association Rules. In ACM SIGKDD'02, EDmonton, Alberta, Canada (2002) 462-468
5. Bronnimann, H., Chen, B., et al: Efficient Data Reduction with EASE, In ACM SIGKDD'03, Washington, D.C., USA (2003) 59-68
6. Parthasarathy, S.: Efficient Progressive Sampling for Association Rules. In ICDE'02, Maebashi City, Japan (2002) 354-361
7. Jia, C-Y., Gao, X-P.: Multi-scaling Sampling: An Adaptive Sampling Method for Discovering Approximate Association Rules, to be appeared, Journal of Computer Science and Technology.
8. Toivonen, H.: Sampling Large Databases for Association Rules. In VLDB'96, Mumbai (Bombay), India (1996) 134-145
9. Zaki, M. J., Parthasarathy, S., Li, W., Ogihara, M.: Evaluation of Sampling for Data Mining of Association Rules. In Proceeding of the 7th Workshop on Research Issues in Data Engineer, Birmingham, UK (1997) 42-50
10. Watanabe, O.: Simple Sampling Techniques for Discovery Science. IEICE Trans. Information and Systems, E83-D(1) (2000) 19-26
11. John, G. H., Langley, P.: Static Versus Dynamic Sampling for Data Mining. In ICDM'96, AAAI Press (1996) 367-370
12. Domingo, C., Gavalda, R., Watanabe, O.: Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms. Data Mining and Knowledge Discovery, An International Journal, Vol. 6(2). (2002) 131-152
13. Breiman, L.: Bagging Predictors. Machine Learning, Vol. 24. (1996) 123-140
14. Breiman, L.: Bias, Variance, and Arcing Classifiers. The Annals of Statistics, Vol. 26(3). (1998) 801-849
15. Dietterich, T. G.: Ensemble Methods in Machine Learning. Lecture Notes in Computer Science, Vol. 1857. Springer-Verlag, Berlin Heidelberg New York (2000) 1-15
16. Krogh, A., Vedelsby, J.: Neural Network Ensembles, Cross Validation, and Active Learning. Advances in Neural Infromation Processing Systems 7 (1995) 231-238
17. Krogh, A., Vedelsby, J.: Neural Network Ensembles, Cross Validation, and Active Learning. Advances in Neural Infromation Processing Systems (2000) 267-279
18. Esposito, R., Saitta, L.: Monte Carlo Theory As An Explanation of Bagging and Boosting. In IJCAI'03, Acapulco, Mexico (2003) 499-504
19. Valiant, L. G., A Theory of the Learnable. Communications of the ACM 27. (1984) 1134-1142
20. Vitter, J. S.: An Efficient Algorithm for Sequential Random Sampling. In ACM Transactions Mathematical Software, Vol. 13(1). (1987) 58-67.