Applying data mining techniques to address critical process optimization needs in advanced manufacturing

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2014, Pages 1739-1748

  Zheng, Li ^a   Zeng, Chunqiu ^a   Li, Lei ^a   Jiang, Yexi ^a   Xue, Wei ^a   Li, Jingxuan ^a   Shen, Chao ^a   Zhou, Wubai ^a   Li, Hongtai ^a   Tang, Liang ^a   Li, Tao ^a   Duan, Bing ^b   Lei, Ming ^b   Wang, Pengnian ^b  

^a The Department of Electrical and Computer Engineering   (United States)

^b Sichuan COC Display Device Co LTD   (China)

Author keywords

advanced manufacturing; big data; data mining platform; process optimization

Indexed keywords

EID: 84907028824     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2623330.2623347     Document Type: Conference Paper

References (32)

1. R. Belz and P. Mertens. Combining knowledge-based systems and simulation to solve rescheduling problems. Decision Support Systems, 17(2):141-157, 1996.
2. I. Chen. Planning for erp systems: analysis and future trend. Business process management journal, 7(5):374-386, 2001.
3. W. Chen, S. Tseng, and C. Wang. A novel manufacturing defect detection method using association rule mining techniques. Expert systems with applications, 29(4):807-815, 2005.
4. C. Davis, F. Gerick, V. Hintermair, C. Friedel, K. Fundel, R. Küffner, and R. Zimmer. Reliable gene signatures for microarray classification: assessment of stability and performance. Bioinformatics, 22(19):2356-2363, 2006.
5. C. Ding and H. Peng. Minimum redundancy feature selection from microarray gene expression data. Journal of bioinformatics and computational biology, 3(02):185-205, 2005.
6. G. Fang, G. Pandey, W. Wang, M. Gupta, M. Steinbach, and V. Kumar. Mining low-support discriminative patterns from dense and high-dimensional data. TKDE, 24(2):279-294, 2012.
7. C. Groger, F. Niedermann, H. Schwarz, and B. Mitschang. Supporting manufacturing design by analytics, continuous collaborative process improvement enabled by the advanced manufacturing analytics platform. In CSCWD, pages 793-799. IEEE, 2012.
8. C. Gröger, F. Niedermann, and B. Mitschang. Data mining-driven manufacturing process optimization. In Proceedings of the World Congress on Engineering, volume 3, pages 4-6, 2012.
9. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explorations, 2009.
10. J. Han, M. Kamber, and J. Pei. Data mining: concepts and techniques. Morgan kaufmann, 2011.
11. T. Hastie, R. Tibshirani, J. Friedman. The elements of statistical learning, volume 2. 2009.
12. A. Kalousis, J. Prados, and M. Hilario. Stability of feature selection algorithms: a study on high-dimensional spaces. Knowledge and information systems, 12(1):95-116, 2007.
13. J. Kletti. Manufacturing Execution Systems (MES). Springer, 2007.
14. D. Lei, M. A Hitt, and J. D Goldhar. Advanced manufacturing technology: organizational design and strategic flexibility. Organization Studies, 17(3):501-523, 1996.
15. T. Li and C. Ding. Weighted ensemble clustering. In SIAM Data Mining, 2008.
16. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In SIGKDD, 1998.
17. S. Loscalzo, L. Yu, and C. Ding. Consensus group stable feature selection. In SIGKDD, pages 567-576, 2009.
18. MILK. http://pythonhosted.org/milk.
19. MLC++. http://www.sgi.com/tech/mlc.
20. S. Oh, J. Han, and H. Cho. Intelligent process control system for quality improvement by data mining in the process industry. In Data mining for design and manufacturing, pages 289-309. Springer, 2001.
21. S. Owen, R. Anil, T. Dunning, and E. Friedman. Mahout in Action. Manning, 2011.
22. R. Palaniswamy and T. Frank. Enhancing manufacturing performance with erp systems. Information systems management, 17(3):43-55, 2000.
23. Z. Prekopcsak, G. Makrai, T. Henk, and C. Gaspar-Papanek. Radoop: Analyzing big data with rapidminer and hadoop. In RCOMM, 2011.
24. M. Robnik-Šikonja and I. Kononenko. Theoretical and empirical analysis of relieff and rrelieff. Machine learning, 53(1-2):23-69, 2003.
25. L. Shen, F. EH Tay, L. Qu, and Y. Shen. Fault diagnosis using rough sets theory. Computers in Industry, 43(1):61-72, 2000.
26. V. A Skormin, V. I Gorodetski, and L. J Popyack. Data mining technology for failure prognostic of avionics. TAES, 38(2):388-403, 2002.
27. A. Topchy, A. K Jain, and W. Punch. A mixture model of clustering ensembles. In SDM, pages 379-390, 2004.
28. T. D Wall, J. M Corbett, R. Martin, C. W Clegg, and P. R Jackson. Advanced manufacturing technology, work design, and performance: A change study. Journal of Applied Psychology, 75(6):691, 1990.
29. A. Woznica, P. Nguyen, and A. Kalousis. Model mining for robust feature selection. In SIGKDD, pages 913-921, 2012.
30. L. Yu, J. Zheng, B. Wu, B. Wang, C. Shen, L. Qian, and R. Zhang. Bc-pdm: Data mining, social network analysis and text mining system based on cloud computing. In SIGKDD, 2012.
31. L. Yu, C. Ding, and S. Loscalzo. Stable feature selection via dense feature groups. In SIGKDD, pages 803-811, 2008.
32. C. Zeng, Y. Jiang, L. Zheng, J. Li, L. Li, H. Li, C. Shen, W. Zhou, T. Li, B. Duan, M. Lei, and P. Wang. FIU-Miner: A Fast, Integrated, and User-Friendly System for Data Mining in Distributed Environment. In SIGKDD, 2013.