Entropy-based associative classification algorithm for mining manufacturing data

International Journal of Computer Integrated Manufacturing

Volumn 21, Issue 7, 2008, Pages 825-838

  Siradeghyan, Y ^a   Zakarian, A ^b   Mohanty, P ^b  

^a MICROSOFT RESEARCH   (United States)

^b Department of Electrical and Computer Engineering   (United States)

Author keywords

Association rules; Data mining; Hybrid thermal spray process

Indexed keywords

ASSOCIATION RULES; ASSOCIATIVE CLASSIFICATION; CASE STUDIES; CERAMIC MOULDS; DATA MINING; DATA SETS; DECISION OUTCOME; EFFECTIVELY CONTROL; HYBRID THERMAL SPRAY PROCESS; INFORMATION ENTROPY; INPUT AND OUTPUT; LARGE DATABASES; MANIPULATION TECHNIQUES; SPRAY PROCESSING;

ASSOCIATIVE PROCESSING; CLASSIFICATION (OF INFORMATION); CONTROL THEORY; DATABASE SYSTEMS; DECISION SUPPORT SYSTEMS; FORMING; INFORMATION MANAGEMENT; LAWS AND LEGISLATION; METAL FORMING; METALS; MINING; PESTICIDES; PRODUCTION CONTROL; SEARCH ENGINES; SET THEORY; SMELTING; SPRAYING; THERMAL SPRAYING;

PROCESS CONTROL;

EID: 51849102990     PISSN: 0951192X     EISSN: 13623052     Source Type: Journal    
DOI: 10.1080/09511920801915200     Document Type: Article

References (65)

1. Agrawal, R. and Srikant, R., 1994. Fast algorithm for mining association rules. In: Proceedings of the 20th VLDB Conference. Morgan Kaufmann, Santiago, Chile, 487-499.
2. Aha, D.W., 1992. Tolerating noisy, irrelevant and novel attributes in instance based learning algorithms. International Journal of Man-Machine Studies, 36, 267-287.
3. Belashchenko, V.E. and Baranovski, V.E., 1999. Thermal spray systems. US Patent No. 5932293.
4. Błaszczynski, J., Greco, S., and Słowinski, R., 2007. Multi-criteria classification - a new scheme for application of dominance-based decision rules. European Journal of Operational Research, 181, 1030-1044.
5. Blum, A.L. and Rivest, R.L., 1992. Training a 3-node neural networks is NP-complete. Neural Networks, 5, 117-127.
6. Breiman, L., et al., 1984. Classification and regression trees. Belmont, CA: Wadsworth International Group.
7. Burges, C.J., 1998. A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery, 2 (2), 121-167.
8. Cantu-Paz, E. and Kamath, C., 2003. Inducing oblique decision trees with evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 7 (1), 54-68.
9. Cetegen, M. and Yu, W., 1999. In-situ particle temperature, velocity, and size measurements in DC arc plasma thermal sprays. Journal of Thermal Spray Technology, 8 (1), 57-67.
10. Chen, Y. and Wang, J.Z., 2003. Support vector learning for fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems, 11, 716-728.
11. Chen, G., et al., 2006. A new approach to classification based on association rule mining. Decision Support Systems, 42, 674-689.
12. Clark, P. and Boswell, R., 1989. The CN2 induction algorithm. Machine Learning, 3 (4), 261-283.
13. Coenan, F. and Leng, P., 2007. The effct of threshold values on association rule based classification accuracy. Data and Knowledge Engineering, 60, 345-360.
14. Domingos, P. and Pazzani, M., 1996. Beyond independence: conditions for the optimality of the simple Bayesian classifier. In: Proceedings of 13th International Conference on Machine Learning. Los Altos, CA, 105-112.
15. Feng, C.-X. and Wang, X.-F., 2002. Development of empirical models for surface roughness prediction in finish turning. International Journal of Advanced Manufacturing Technology, 20 (5), 348-356.
16. Feng, C.-X. and Wang, X.-F., 2003. Surface roughness predictive modeling: neural networks versus regression. IIE Transactions, 35 (1), 11-27.
17. Fincke, I.R., et al., 2001. Diagnostics and control in the thermal spray process. Surface Coating Technology, 146-147, 537-543.
18. Friedman, J., Yun, Y., and Kohavi, R., 1996. Lazy decision trees. In: Proceedings of 13th National Conference on Artificial Intelligence.
19. Garcia-Molina, H., Ullman, J.D., and Widom, J.D., 2001. Database systems: the complete book. Upper Saddle River, NJ: Prentice Hall.
20. Gehrke, J., Ramakrishnan, R., and Ganti, V., 1998. Rain-Forest a framework for fast decision tree construction of large datasets. In: International Conference on Very Large Databases, August. New York, NY, 416-427.
21. Grzymala-Busse, J.W., 1997. A new version of the rule induction system LERS. Fundamenta Informaticae, 31, 27-39.
22. Gehrke, J.E., Ramakrishnan, R., and Ganti, V., 2000. RainForest: A framework for fast decision tree construction of large datasets. Data Mining and Knowledge Discovery, 4, 127-162.
23. Godino-Llorente, J.I. and Gómez-Vilda, P., 2004. Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors. IEEE Transactions on Biomedical Engineering, 51 (2), 380-384.
24. Han, J., Pei, J., and Yin, Y., 2000. Mining frequent patterns without candidate generation. In: SIGMOD'00, Dallas, TX, May, 1-12.
25. Han, J. and Kamber, M., 2006. Data mining: concept and techniques. San Francisco, CA: Morgan Kaufmann.
26. Hollis, K. and Neiser, R., 1998. Particle temperature and flux measurement utilizing a nonthermal signal correction process. Journal of Thermal Spray Technology, 7 (3), 392-402.
27. Kim, K. and Han, I., 2001. The extraction of trading rules from stock market data using rough sets. Expert Systems, 18 (4), 194-202.
28. Kohavi, R. and John, G.H., 1997. Wrappers for feature subset selection. Artificial Intelligence, 97 (1-2), 273-324.
29. Kohonen, T., et al., 2000. Self organization of a massive document collection. IEEE Transactions on Neural Networks, 11, 574-585.
30. Kosikowski, D., Batalov, M., and Mohanty, P.S., 2005a. Functionally graded coatings by HVOF-arc hybrid spray gun. In: Proceedings of International Thermal Spray Conference: ITSCO5, 444-449.
31. Kosikowski, D., Batalov, M., and Mohanty, P.S., 20005b. In-flight particle characterization of HVOF-arc hybrid gun. In: Proceedings of International Thermal Spray Conference: ITSCO5, 785-790.
32. Kusiak, A., 2000. Decomposition in data mining: an industrial case study. IEEE transactions on electronics packaging manufacturing, 23 (4), 345-353.
33. Kusiak, A., 2001. Rough set theory: A data mining tool for semiconductor manufacturing. IEEE Transactions on Electronics Packaging Manufacturing, 24 (1), 44-50.
34. Kusiak, A., Kern, J.A., Kernstine, K.H., and Tseng, B.T., 2000. Autonomous decision-making: a data mining approach. IEEE Transactions on Information Technology in Biomedicine, 4 (4), 274-285.
35. Kusiak, A. and Kurasek, C., 2001a. Data mining of printed-circuit board defects. IEEE Transactions on Robotics and Automation, 17 (2), 191-197.
36. Kusiak, A., Law, I.H., and Dick, M., 2001b. The G-algorithm for extraction of robust decision rules: children's postoperative intra-atrial arrhythmia case study. IEEE Transactions on Information Technology in Biomedicine, 5 (3), 234-255.
37. Li, J., 2006. Robust rule-based prediction. IEEE Transactions on Knowledge and Data Discovery, 18 (8), 1043-1054.
38. Li, W., Han, J., and Pei, J., 2001. CMAR: Accurate and efficient classication based on multiple class association rules. ICDM 2001. San Jose, CA: IEEE Computer Society, 369-376.
39. Liu, H. and Yu, L., 2005. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17 (4), 491-502.
40. Liu, B., Hsu, W., and Ma, Y., 1998. Integrating classification and association rule mining. Knowledge Discovery and Data Mining, New York, 80-86.
41. Mates, S.P., et al., 2002. Calibration of a two-color imaging pyrometer and its use for particle measurements in controlled air plasma spray experiments. Journal of Thermal Spray Technology, 11 (2), 195-205.
42. Michalski, R.S., Bratko, I., and Kubat, M., eds. 1998. Machine Learning and Data Mining. New York: Wiley.
43. Müller, K.-R., et al., 2001. An introduction to kernel-based learning algorithms. IEEE Transactions Neural Networks, 12, 181-202.
44. Mehta, M., Agrawal, R., and Rissanen, J., 1996. SLIQ: A fast scalable classifier for data mining. In: International Conference Extending Database Technology, Avignon, France, March, 18-32.
45. Murthy, S.K., 1998. Automatic construction of decision trees from data: a multidisciplinary survey. Data Mining and Knowledge Discovery, 2, 345-389.
46. Quinlan, J.R., 1993. C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
47. Quinlan, J.R., 1986. Induction of decision trees. Machine Learning, 1 (1), 81-106.
48. Ohrn, A., Ohno-Machado, L., and Rowland, T., 1998. Building manageable rough set classifiers. In: Proceedings of AMIA Symposium, November, Orlando, FL, 543-547.
49. Pawlak, Z., 1982. Rough sets. International Journal of Information and Computer Sciences, 11, 341-356.
50. Pawlak, Z., 1991. Rough sets - theoretical aspects of reasoning about data. Boston: Kluwer Academic: Boston.
51. Pawlak, Z., 1997. Rough set approach to knowledge-based decision support. European Journal of Operational Research, 99, 48-57.
52. Racket, C.M. and Settles, G.S., 1996. Independent control of HVOF particle velocity and temperature. In: C.C. Bemdt, ed. Thermal spray practical solutions for engineering problems. Materials Park, OH: ASM International, 665-673.
53. Rak, R., Kurgan, L., and Reformat, M., 2008. A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation. Data and Knowledge Engineering, 64 (1), 171-197.
54. Rauszer, C., 1991. Reducts in information systems. Fundamenta Informaticae, 15, 1-12.
55. Ripley, B.D. and Ripley, R.M., 1997. Neural networks as statistical methods in survival analysis. In: R. Dybowski and V. Grant, eds. Artificial neural networks: prospects for medicine. Texas: Landes Biosciences.
56. Ruggieri, S., 2002. Efficient C 4.5. IEEE Transactions on Knowledge and Data Engineering, 14 (2), 438-444.
57. Shafer, J., Agrawal, R., and Mehta, M., 1996. SPRINT: A scalable parallel classifier for data mining. In: International Conference on Very Large Databases, Bombay. India, 544-555.
58. Shannon, C.E., 1948. A Mathematical theory of communication. The Bell System Technical Journal, 27, 379-423, 623-656.
59. Slowinski, R., 1992. Intelligent decision support: Handbook of applications and advances of rough set theory. System Theory, Knowledge Engineering and Problem Solving, Vol. 11, Kluwer.
60. Smola, J., et al., eds, Advances in large margin classifiers (neural information processing). Cambridge, MA: MIT Press.
61. Vapnik, V., 1995. The nature of statistical learning theory. New York: Springer-Verlag.
62. Vapnik, V., 1998. Statistical learning theory. New York: Wiley.
63. Vattulainen, I., et al., 2001. Novel method for in-flight particle temperature and velocity measurements in plasma spraying using a single CCD camera. Journal of Thermal Spray Technology, 10 (1), 94-104.
64. Yang, Z.R., Platt, M.B., and Platt, H.D., 1999. Probabilistic neural networks in bankruptcy prediction. Journal of Business Research, 44, 67-74.
65. Yin, X. and Han, J., 2003. CPAR: Classification based on predictive association rules. In: Proceedings of 2003 SIAM International Conference Data Mining (SDM'03), May, San Francisco, CA.