Adaptive data reduction for large-scale transaction data

European Journal of Operational Research

Volumn 188, Issue 3, 2008, Pages 910-924

  Li, Xiao Bai ^a   Jacob, Varghese S ^b  

^a UNIVERSITY OF MASSACHUSETTS LOWELL   (United States)

^b UNIVERSITY OF TEXAS AT DALLAS   (United States)

Author keywords

Data mining; Data reduction; Distance measure; Genetic algorithms

Indexed keywords

DATA MINING; GENETIC ALGORITHMS; LARGE SCALE SYSTEMS; PROBLEM SOLVING; QUADRATIC PROGRAMMING;

DATA SETS; DISTANCE MEASURES;

DATA REDUCTION;

EID: 38649113213     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2007.08.008     Document Type: Article

References (26)

1. Agrawal R., and Srikant R. Fast algorithms for mining association rules in large databases. Proceedings of the 20th International Conference on Very Large Databases (1994), Morgan Kaufman, San Francisco, CA
2. M. Arlitt, C. Williamson, Web server workload characterization: the search for invariants, In: Proceedings of the 1996 ACM SIGMETRICS Conference on the Measurement and Modeling of Computer Systems, Philadelphia, PA, 1996.
3. Balakrishnan P.V., and Jacob V.S. Genetic algorithms for product design. Management Science 42 8 (1996) 1105-1117
4. Bhattacharyya S. Direct marketing performance modeling using genetic algorithms. INFORMS Journal on Computing 11 3 (1999) 248-257
5. Brill F.Z., Brown D.E., and Martin W.N. Fast genetic selection of features for neural network classifiers. IEEE Transactions on Neural Networks 3 2 (1992) 324-328
6. J. Catlett, 1991. Megainduction: Machine learning on very large databases, Doctoral Dissertation. Basser Department of Computer Science, University of Sydney Australia.
7. Domingo C., Gavalda R., and Watanbe O. Adaptive sampling methods for scaling up knowledge discovery algorithms. Data Mining and Knowledge Discovery 6 2 (2002) 131-152
8. A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrke, 2002. Privacy preserving mining of association rules, In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada.
9. Fayyad U., and Uthurusamy R. Evolving data mining into solutions for insights. Communications of the ACM 45 8 (2002) 28-31
10. Gorla N., and Krehbiel S. Summers rubber company designs its data warehouse. Interfaces 29 2 (1999) 104-117
11. Hettich S., and Bay S.D. The UCI KDD Archive (1999), Deptartment of Information and Computer Science, University of California, Irvine, CA
12. Holland J.H. Adaptation in Natural and Artificial Systems (1975), The University of Michigan Press, Ann Arbor, MI
13. Ishibuchi H., Nakashima T., and Nii M. Genetic-algorithm-based instance and feature selection. In: Liu H., and Motoda H. (Eds). Instance Selection and Construction for Data Mining (2001), Kluwer Academic, Norwell, MA
14. Li X.-B. Data reduction via adaptive sampling. Communications in Information and Systems 2 1 (2002) 53-68
15. Li X.-B. A scalable decision tree system and its application in pattern recognition and intrusion detection. Decision Support Systems 41 1 (2005) 112-130
16. Pakath R., and Zaveri J.S. Specifying critical inputs in a genetic algorithm-driven decision support system: An automated facility. Decision Sciences 26 6 (1995) 749-779
17. Pyle D. Data Preparation for Data Mining (1999), Morgan Kaufman, San Francisco, CA
18. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufman, San Mateo, CA
19. Reinartz T. A unifying view on instance selection. Data Mining and Knowledge Discovery 6 2 (2002) 191-210
20. S.J. Rizvi, J.R. Haritsa, Maintaining data privacy in association rule mining, In: Proceedings of the 28th Very Large Data Base Conference, Hong Kong, China, 2002.
21. Shaw M.J., Subramaniam C., Tan G.W., and Welge M.E. Knowledge management and data mining for marketing. Decision Support Systems 31 1 (2001) 127-137
22. Shim J.P., Warkentin M., Courtney J.F., Power D.J., Sharda R., and Carlsson C. Past, present, and future of decision support technology. Decision Support Systems 33 2 (2002) 111-126
23. Siedlecki W., and Sklansky J. A note on genetic algorithms for large scale feature selection. Pattern Recognition Letters 10 5 (1989) 335-347
24. Susmaga R., Slowinski R., Greco S., and Matarazzo B. Generation of reducts and rules in multi-attribute and multi-criteria classification. Control and Cybernetics 29 4 (2000) 969-988
25. Susmaga R. Reducts and constructs in attribute reduction. Fundamenta Informaticae 61 2 (2004) 159-181
26. Witten I.H., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (2000), Morgan Kaufman, San Francisco, CA