Decomposition in data mining: An industrial case study

IEEE Transactions on Electronics Packaging Manufacturing

Volumn 23, Issue 4, 2000, Pages 345-353

  Kusiak, Andrew ^a  

^a UNIVERSITY OF IOWA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATABASE SYSTEMS; DECISION MAKING; INTEGRATED CIRCUITS; KNOWLEDGE ACQUISITION; SEMICONDUCTOR DEVICE MANUFACTURE;

QUALITY ENGINEERING;

DATA MINING;

EID: 0034290324     PISSN: 1521334X     EISSN: None     Source Type: Journal    
DOI: 10.1109/6104.895081     Document Type: Article

References (30)

1. M. J. A. Berry and G. Einoff, Data Mining Techniques: For Marketing, Sales, and Customer Support. New York: Wiley, 1997.
2. R. Groth, Data Mining: A Hands-On Approach for Business Professionals. Upper Saddle River, NJ: Prentice Hall, 1998.
3. T. Mitchell, Machine Learning. New York: MacGraw-Hill, 1997.
4. J. R. Quinlan, "Induction of decision trees," Mach. Learn., vol. 1, no. l, pp. 81-106, 1986.
5. R. S. Michalski, I. Mozetic, J. Hong, and N. Lavrac, "The multi-purpose incremental learning system AQ15 and its testing application to three medical domains," in Proceedings of the 5th National Conference on Artificial Intelligence. Palo Alto, ÇA: AAAI, 1986, pp. 1041-1045.
6. P. Domingos and M. Pazzani, "Beyond independence: Conditions for the optimality of the simple Bayesian classifier," in Machine Learning: Proceedings of the Thirteenth International Conference. Los Altos, CA: Morgan Kaufmann, 1996, pp. 105-112.
7. R. Kohavi, "Wrappers for performance enhancement and oblivious decision graphs," Ph.D. dissertation, Comput. Sci. Dept., Stanford Univ., Stanford, CA, 1995.
8. J. Friedman, Y. Yun, and R. Kohavi, "Lazy decision trees," in Proceedings of the Thirteenth National Conference on Artificial Intelligence. Boston, MA: AAAI Press and MIT, 1996.
9. J. R. Quinlan, C4.5: Programs for Machine Learning. Los Altos, CA: Morgan Kaufmann, 1993.
10. P. Clark and R. Bos well, "The CN2 induction algorithm," Machine Learning, vol. 3, no. 4, pp. 261-283, 1989.
11. D. W. Aha, "Tolerating noisy, irrelevant and novel attributes in instancebased learning algorithms," Int. J. Man-Mach. Stud., vol. 36, no. 2, pp. 267-287, 1992.
12. S. K. Murthy and S. Salzberg, "A system for the induction of oblique decision trees,"J. Artif. Intell. Res., vol. 2, no. 1, pp. 1-33, 1994.
13. P. Auer, R. Holte, and W. Maass, "Theory and application of agnostic PAC-learning with small decision trees," in ECML-95: Proceedings of 8th European Conference on Machine Learning, A. Prieditis and S. Russell, Eds. New York: Springer Verlag, 1995.
14. J. W. Grzymala-Busse, "A new version of the rule induction system LERS," Fund. Inform., vol. 31, pp. 27-39, 1997.
15. T. Y Lin and N. Cecerone, Eds., Rough Sets and Data Mining. Boston, MA: Kluwer, 2000.
16. J. G. Carbonell, Ed., Machine Learning: Paradigms and Methods. Cambridge, MA: MIT Press, 1990.
17. R. S. Michalski, I. Bratko, and M. Kubat, Machine Learning and Data Mining: Methods and Applications. New York: Wiley, 1998.
18. P. Langley and H. A. Simon, "Applications of machine learning and rule induction," Commun. ACM, vol. 38, no. 11, pp. 55-64, 1995.
19. A. Kusiak, Computational Intelligence in Design and Manufacturing. New York: Wiley, 2000.
20. Z. Pawlak, "Rough sets," Int. J. Inform. Comput. Sci., vol. 11, no. 5, pp. 341-356, 1982.
21. _, Rough Sets: Theoretical Aspects of Reasoning About Data. Boston, MA: Kluwer, 1991.
22. M. J. Zaki and C.-T. Ho, Eds., Large-Scale Parallel Data Mining. New York: Springer-Verlag, 2000.
23. R. Grossman, S. Kasif, R. Moore, D. Rocke, and J. Ullman, "Data mining research: Opportunities and challenges. Report of three NSF workshops on mining large, massive, and distributed data," Tech. Rep., http://www.ncdm.uic.edu/M3D-final-report.htm, 1999.
24. Y Guo and J. Sutiwaraphun, "Knowledge probing in distributed data mining," in Proc. 4h Int. Conf. Knowledge Discovery Data Mining, 1988, http://www.eecs.wsu.edu/ hillol/kdd98ws.html.
25. M. J. Zaki, C.-T. Ho, and R. Agrawal, "Scalable parallel classification for data mining on shared-memory multiprocessors," in Proc. IEEE Int. Conf. Data Eng., Sydney, Australia, 1999, http://www.cs.rpi.edu/ zaki/papers.html#WKDD99, pp. 198-205.
26. T. G. Dietterich, "Machine learning research: Four current directions," AI Mag., vol. 18, no. 4, pp. 97-136, 1997.
27. S. Stolfo, A. Prodromidis, S. Tselepis, W. Lee, W. Fan, and P. Chan, "JAM: Java agents for meta-learning over distributed databases," in Proc. 4rth Int. Conf. Knowledge Discovery Data Mining, 1997, pp. 74-81.
28. J. Stefanowski, "On rough set based approaches to induction of decision rules," in Rough Sets in Knowledge Discovery, L. Polkowski and A. Skowron, Eds. Heidelberg, Germany: Physica-Verlag, 1998, pp. 501-529.
29. A. Kusiak, J. A. Kern, K. H. Kernstine, and T. L. Tseng, "Autonomous decision-making: A data mining approach," IEEE Trans. Inform. Technol. Biomed., to be published.
30. P. Clark and R. Boswell, "Rule induction with CN2: Some recent improvements," in Proceedings of the Fifth European Working Session on Learning, EWSL'91, Y Kondratoff, Ed. Berlin, Germany: SpringerVerlag, 1991, pp. 151-163.