Volume -I , Issue -III, February 2014

DATA MINING INDUSTRIAL APPLICATIONSne

DIVYAM SINGH

Novel, advanced sensors, dynamic development of information technologies as well as modern high-performance computers applied in different fields of human activity result in large amount of data. Consequently, these data, grouped in the data sets are both large and complex. The complexity come fr om the several mutually excluding factors like acquisition with different sensors at various times, frequencies or resolutions. The increasing size and complexity of data in different practical, often industrial branches stands the challenging problem for nowadays scientific disciplines.

Industrial Applications, visualization and statistics

1. Bartok J., Habala O., Bednar P ., Gazak M. & Hluchy L. (2010). Data mining and integration for predicting significant meteorogical phenomena. International Conference on Computational Science, (ICCS 2010), Procedia Computer Science 1, Elsevier, 37-46
2. Bellazzi R., & Zupan B., (2008). Predictive data mining in clinical medicine: Current issues and guidelines. International Journal of Medical Informatics 77, 81-97
3. Fayyad, U. M., Shapiro, G. P ., & Smyth P ., (1999). The KDD process for extracting useful knowledge from volume of data. Communications of the ACM, 39, 27-34
4. Gebus, S. & Leiviska, K. (2009). Knowledge acquisition for decision support systems on an electronic assembly line. Expert Systems with Applications 36 (1) 93–101
5. Han, J. & Kamber, M. (2001). Data mining: concepts and techniques. Morgan Kaufmann, USA
6. Bellazzi R., & Zupan B., (2008). Predictive data mining in clinical medicine: Current issues and guidelines. International Journal of Medical Informatics 77, 81-97
7. Bartok J., Habala O., Bednar P ., Gazak M. & Hluchy L. (2010). Data mining and integration for predicting significant meteorogical phenomena. International Conference on Computational Science, (ICCS 2010), Procedia Computer Science 1, Elsevier, 37-46
8. Bertino, E., Catania, B. Caglio, E. (1999). (Applying data mining techniques to wafer manufacturing), in: Zytkow , J.M., Rauch, J. (Eds.), PKDD99, LNAI, vol. 1704, Springer-V erlag, Berlin, 41–50
9. Lee, M. H. (1993). Knowledge based factory . Artificial Intelligence Engineering 8, 109–125
10. Simoudis, E. (1996). Reality check for data mining. IEEE Expert, 26–33 Smith, J., (2003). Data mining with C# and ADO.NET , www.devsource.com T ang, Z., (2005). Data Mining with SQL Server 2005,
11. John Wiley & Sons Wang, K. (2006). Data mining in manufacturing: the nature and implications. in: Wang, Kovacs, G., Wozny , M. et al. (Eds.), International Federation for Information Processing (IFIP) Knowledge
12. Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Management, vol. 207, Springer-V erlag, Boston, 1–10
13. Last, M. & Kandel, A. (2004). Discovering useful and understandable patterns in manufacturing data. Robotics and Autonomous Systems 49, 37–152
14. Yu, J., Xi, L. & Zhou, X. (2008). Intelligent monitoring and diagnosis of manufacturing processes using an integrated approach of KBANN and GA. Computers in Industry 59, 489–501
15. Zawadzki, M., (2005). SQL Server 2005, MIKOM
16. Zarski, A., (2006). Data mining using SQL Server 2005, www.codeguru.pl
17. Buddhakulsomsiri, J., Siradeghyan, Y ., Zakarian, A., & Li, X. (2006). Association rule generation algorithm for mining automotive warranty data. International Journal of Production Research, 44(14), 2749–2770
18. Chien, C., Wang, W . & Cheng, J. (2007). Data mining for yield enhancement in semiconductor manufacturing and an empirical study . Expert Systems with Applications 33, 192–198
19. Durán, O., Rodriguez, N. & Consalter, L.A. (2010). Collaborative particle swarm optimization with a data mining technique for manufacturing cell design. Expert Systems with Applications 37, 1563–1567
20. Journal of Manufacturing Science and Engineering 128, 969–976
21. Irani, K.B., Cheng, J., Fayyad, U.M. et al., (1993). Applying machine learning to semiconductor manufacturing. IEEE Expert 8 (1), 41–47
22. Jacobson, R. & Misner, S. (2005). Microsoft SQL Sewer 2005 Analysis Services. Step by step. PromiseKamath, C. (2009). Scientific Data Mining. A practical perspective. Society for Industrial and Applied Mathematics, Philadelphia
23. Kang, P ., Lee H-J., Cho, S., Kim, D., Park, J., Park, C-K, & Doh, S. (2009). A virtual metrology system for semiconductor manufacturing. Expert Systems with Applications 36, 12554– 12561
24. Kumar, S., Nassehi, A. & Newman, S.T . et al. (2007). Process Control in CNC manufacturing for discrete components: a STEP-NC compliant framework. Robotics and Computer Integrated Manufacturing 23, 667–676
25. Liu, C. (2007). A data mining algorithm for designing the conventional cellular manufacturing systems. in: Orgun, M.A. , Thornton, J. (Eds.), AI 2007, LNAI, 4830, Springer-V erlag, Berlin, 715–720
26. Maki, H. & T eranishi, Y . (2001). Development of automated data mining system for quality control in manufacturing. in: Kambayashi, Y ., Winiwarter , W ., Arikawa, M. (Eds.), DaW ak, LNCS 2114, Springer-V erlag, Berlin, 93-100
27. Seng J., & Chen T ., (2010). An analytic approach to select data mining for business decision. Experts Systems with Applications 37, 8042-8057
28. Choudhary , A. K., Harding, J. A., & Tiwari, M. K. (2009). Data mining in manufacturing: a review based on the kind of knowledge. Intell Manuf, 20, 501-521
29. Çiflikli, C. & Kahya-Özyirmidokuz, E. (2010). Implementing a data mining solution for enhancing carpet manufacturing productivity . Knowledge-Based Systems (article in press)
30. Charaniya, S., Le, H., Rangwala, H., Mills, K., Johnson, K., Karypis, G. & Hu W . (2010). Mining manufacturing data for discovery of high productivity process characteristics. Journal of Biotechnology , 147(3-4), 186-97
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31. Duebel, C. (2003). Application of Data Mining T echniques to Industrial Processes for Improved Business Performance, AP ACT Conference
32. Gibbons, W ., Ranta, M., Scott, T . et al. (2000). Information management and process improvement using data mining techniques. in: Loganantharaj, R., et al. (Eds.), IEA/AIE 2000, LNAI, 1821, SpringerV erlag, Berlin, 93–98
33. Harding, J.A., Shahbaz, M., Srinivas, S. & Kusiak, A. (2006). Data mining in manufacturing: a review .
34. Hsu, C. (2009). Data mining to improve industrial standards and enhance production and marketing: an empirical study in apparel industry . Expert Systems with Applications 36 (3), 4185–4191
35. Huang, H. & Wu, D. (2005). Product quality improvement analysis using data mining: a case study in ultra-precision manufacturing industry . in: Wang, L. Jin Y . (Eds.), FSKD 2005, LNAI, 3614, SpringerV erlag, Berlin, 577–580
36. Mitra, S., Pal., S. K., & Mitra P ., (2002). Data mining in soft computing framework: A survey . IEEE Transactions on Neural Networks, 13(1), 3-14
37. Rokach, L. & Maimon, O. (2006). Data mining for improving the quality of manufacturing: a feature set decomposition approach. Journal of Intelligent Manufacturing 17 (3), 285– 299