|
Agresti, A. (1990). Categorical data analysis. Wiley, New York. Ankerst, M., Breunig, M., Kriegel, H.-P. and Sander, J. (1999). OPTICS: ordering points to identify clustering structure. In Proceedings of the ACM SIGMOD Conference, Philadelphia, PA, 49-60 Baker, W.M. (1994). Understand activity-based costing. Industrial Management, 36, 28-30. Bergeret, F. and Gall, C.L. (2003). Yield improvement using statistical analysis of process dates. IEEE Transactions on Semiconductor Manufacturing 16(3), 535-542. Berkhin, P. (2006). A survey of clustering data mining techniques. In Grouping Multidimensional Data, 25-71. Berry, M.A. (2003). Survey of text mining: clustering, classification, and retrieval. Springer. Berry, M.A. and Lindoff, G. (1997). Data mining technique: For marketing, sales, and customer support. Wiley. Bertino, E., Catania, B. and Caglio, E. (1999). Applying data mining techniques to wafer manufacturing. PKDD, 1704, 41-50. Boley, D., Gini, M., Gross, R., Han, S., Hastings, K., Pis, K. G., Kumar, V., Mobasher, B. and Moore, J. (1999). Partitioning-based clustering for web document categorization. Decision Support Systems, 27 (3), 329-341. Braha, D. and Shmilovici, A. (2002). Data mining for improving a cleaning process in the semiconductor industry. IEEE Transactions on Semiconductor Manufacturing, 15(1), 91-101. Braha, D. and Shmilovici, A. (2003). On the use of decision tree induction for discovery of interactions in a photolithographic process. IEEE Transactions on Semiconductor Manufacturing, 16(4), 644-652. Breiman, L., Friedman, J.H., Olshen, R.J. and Stone, C.J. (1984). Classification and regression trees. Wadsworth, Belmont, California. Cadez, I., Smyth, P. and Mannila, H. (2001). Probabilistic modeling of transactional data with applications to profiling, visualization, and prediction. In Proceedings of the 7th ACM SIGKDD, San Francisco, CA. , 37-46. Chan, P.K., Fan, W. Prodromidis, A.L. and Stolfo, S.J. (1999). Distributed data mining in credit card fraud detection. IEEE Intelligent Systems, 14(6), 67-74. Carpenter, G.A. and Grossberg, S. (1987). ART 2: stable self-organization of pattern recognition codes for analog input patterns. Applied Optics, 26, 4919-4930. Carpenter, G.A. and Grossberg, S. (1988). The ART of adaptive pattern recognition by a self-organization neural network. Computer, 21(3), 77-88. Carpenter, G.A. and Grossberg, S. (1990). ART 3: hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. Neural Networks, 3, 129-152. Carpenter, G.A., Grossberg, S. and Reynolds, J.H. (1991). ARTMAP: supervised real-time learning and classification of non-stationary data by a self-organizing neural network. Neural Networks, 4, 565-588. Carpenter, G.A., Grossberg, S. and Rosen, D.B. (1991). Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks, 4, 759-771. Cheeseman, P. and Stutz, J. (1996). Bayesian classification (AutoClass): theory and results. In Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press. Chen, F.L. and Liu, S.F. (2000). A neural-network approach to recognize defect spatial pattern in semiconductor fabrication. IEEE Transaction Semiconductor Manufacturing, 13, 366–372. Chien, C. (2005). Decision analysis and management- a Unison framework for total decision quality enhancement. Yeh-Yeh book gallery, Taipei. Chien, C. and Chen, L. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280-290. Chien, C., Chen, W. and Hsu, S. Requirement estimation for indirect workforce allocation in semiconductor manufacturing (accepted by International Journal of Production Research) Chien, C., Hsiao, A. and Wang, I. (2004). Constructing semiconductor manufacturing performance indexes and applying data mining for manufacturing data analysis. Journal of the Chinese Institute of Industrial Engineers, 21(4), 313-27. Chien, C., Hsu, S. and Chen, C. (1999). An Iterative Cutting procedure for determining the optimal wafer exposure pattern. IEEE Transactions on Semiconductor Manufacturing, 12(3), 375-377. Chien, C., Hsu, S. and Hsu, C (2007). Enhancing competitive advantages and operational excellence for high-tech industry through data mining and digital management. In Liao, T.W. and Triantaphyllou, E., Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications. World Scientific, Singapore, 367-411. Chien, C., Hsu, S. and Deng, J. (2001). A cutting algorithm for optimizing the wafer exposure pattern. IEEE Transactions on Semiconductor Manufacturing, 14(2), 157-162. Chien, C. and Jan, B. (1998). A decision analysis framework and an illustartion of strategic decision making in a semiconductor FAB. Journal of Technology Management. 3(1), 137-156. Chien, C., Lee, P. and Peng, C. (2003). Semiconductor manufacturing data mining for clustering and feature extraction. Journal of Information Management, 10(1), 63-84. Chien, C., Lin, D., Peng, C. and Hsu, S. (2001). Developing data mining framework and methods for diagnosing semiconductor manufacturing defects and an empirical study of wafer acceptance test data in a wafer Fab. Journal of the Chinese Institute of Industrial Engineers, 18(4), 37-48. Chien, C., Wang, W. and Cheng, J. (2007). Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Systems with Applications, 33(1), 1-7. Connors, D.P., Feigin, G.E. and Yao, D.D. (1996). A queueing network model for semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 9(3), 412-427. Cooley, R., Mobasher, B. and Srivastava, J. (1999). Data preparation for mining world wide web browsing. Journal of Knowledge Information Systems, 1(1), 5-32. Cunningham, S.P., Spanos, C.J. and Voros, K. (1995). Semiconductor yield improvement: results and best practices. IEEE Transactions on Semiconductor Manufacturing, 8(2), 103-109. Daskalaki, S. and Kopanas, I. and Avouris, N.M. (2007). Predictive classification with imbalanced enterprise data. In Liao, T.W. and Triantaphyllou, E., Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications. World Scientific, Singapore, 147-187. Davenport, T. and Harris, J. (2006). Competing on analysis, Boston, Harvard Business School Press. Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal statistical Society, Series B, 39(1): 1-38. Devore, J. L. (1995). Probability and statistics for engineering and the sciences, 4th edition, Wadsworth, Belmont, CA. Diaza, Alejandra Campero (2007). A data mining and time Series Integrated Approach for analyzing Semiconductor MES and FDC data to enhance overall usage effectiveness. Master thesis of National Tsing Hua University. Dowsland, K.A. and Dowsland, W.B. (1992). Packing problems. Europe Journal of Operation Research, 56(1), 2–14. Duda, R. and Hart, P.E. (1973). Pattern classification and scene analysis. New York, Wiley. Eick, C.F., Zeidat, N. and Zhao, Z. (2004). Supervised clustering – algorithms and benefits. 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'04), Boca Raton, Florida, November 15-17, 774-776 Eriksen, L. and Prouty, (2001). AMR outlook: enterprise manufacturing intelligence (EMI)--the next generation PIMS. Article from AMR Research Ester, M., Kriegel, H.-P., Jörg, S. and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In international conference on Knowledge Discovery in Databases and Data Mining (KDD-96), Portland, Oregon, August, 1996. Fan, C.M., Guo, R.S., Chen, A., Hsu, K.C. and Wei, C.S. (2001). Data mining and fault diagnosis based on wafer acceptance test data and in-line manufacturing data. IEEE International Semiconductor Manufacturing Symposium, 8(10), 171–174. Fayyad, U., Piatesky-Shapiro, G. and Smyth, P. (1996). From data mining to knowledge discovery: an overview. Advances in Knowledge and Data Mining, AAAI Press, Menlo Park, 1-30. Fowler, A. (2000). The role of AI-based technology in support of the knowledge management value activity cycle. Journal of Strategic Information System, 9(2-3), 107-128 Fowler, J.W., Phojanamongkolkij, N., Cochran, J.K. and Montgomery, D.C. (2002). Optimal batching in a wafer fabrication facility using a multiproduct G/G/c model with batching processing. International Journal of Production Research, 40(2), 275-292. Fraley, C. and Raftery, A. (1999). MCLUST: Software for model-based cluster and discriminant analysis. Tech Report 342, Dept. Statistics, Univ. of Washington. Freeman, J.A., and Skapura, D.M. (1991). Neural networks: algorithm and programming techniques. Addison-Wesley. Friedman, D.J., Hansen, M.H., Nair, V.N. and James, D.A. (1997). Model-free estimation of defect clustering in integrated circuit fabrication. IEEE Transactions on Semiconductor Manufacturing, 10(3), 344-359. Friedman, T. and Hostmann, B. (2004). The Cornerstones of business intelligence excellence, Research Note of Decision Framework, DF-21-9470, 26 April 2004 Fu, Y. (1997). Data mining. IEEE Potentials, 164, 18-20. Gartner Research (2009). Analysts discuss business intelligence challenges and opportunities. Gartner Business Intelligence Summit, 20-22 January in The Hague, Netherlands. Gholamian, M.R. and Fatemi Ghomia, S.M.T. (2007). Meta knowledge of intelligent manufacturing: An overview of state-of-the-art . Applied Soft Computing Journal, 7(1), 1-16. Goldstein, J. M., and Simpson, J. C. (1995). Validity: definitions and applications to psychiatric research. In: Tsuang, M. T., Tohen, M. and Zahner G.E.P. (Eds.), Textbook in psychiatric epidemiology, Wiley-Liss Inc, New York. Guha, S., Rastogi, R. and Shim, K. (1998). Cure: an efficient clustering algorithm for large databases. SIGMOD Rec. 27 (2), 73-84. Han., J. and Kamber, M. (2001). Data mining: concepts and techniques. Morgan Kaufmann Publishers. Hansen, M.H. and Nair, V.N. (1995). Monitoring wafer map from integrated circuit fabrication processes for spatially clustered defects. Technometrics, 39(3), 241-253. Haykin, S. (1995). Neural networks: a comprehensive foundation, 2nd edition, Englewood Cliffs, NJ: Prentice-Hall. Herring, Jan P. (1988). Building a business intelligence system. Journal: Journal of Business Strategy, 9(3), 4 - 9 Hinneburg, A. and Keim, D.A. (1998). An efficient approach to clustering in large multimedia databases with noise. In: Knowledge Discovery and Data Mining, 58-65. Hopfield, J.J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79, 2554-2558. Hopp, W.J. and Spearman M.L. (2000). Factory physics. MGraw-Hill, 2nd edition. Hopp, W. J., Spearman, M.L., Chayet, S. and Donohue, K.L. (2002). Using an optimized queueing network model to support wafer fab design. IIE Transactions, 34(2), 119-130. Hseih, K. (2007). Employing data mining technique to achieve the parameter optimization based on manufacturing intelligence. Journal of the Chinese Institute of Industrial Engineers, 24(4), 309-318. Hsu, J. (2004). Data mining and business intelligence: tools, technology and applications. In M. Raisinghani (Ed.), Business intelligence in the digital economy. London: Idea Group Publishing Hsu, S. and Chien, C. (2007). Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing. International Journal of Production Economics, 107, 88-103. Hsu, S. and Chien, C. (2005). Diagnosing abnormal WAT problems by applying an effective data mining framework. Proceedings of 3rd Modeling and Analysis of Semiconductor Manufacturing Conference, 6-7 October, Singapore, 365-372. Hu, M. and Chang, S. (2003). Translating overall production goals into distributed flow control parameters for semiconductor manufacturing. Journal of Manufacturing Systems, 22(1), 46-63. Huang, C.J. (2007). Clustered defect detection of high quality chips using self-supervised multilayer perception. Expert Systems with Applications, 33(4), 996-1003. Hwang, J.Y and Kuo, W. (2007). Model-based clustering for integrated circuit yield enhancement. European Journal of Operational Research, 178, 143–153 Hwarng, H.B. and Hubele, N.F. (1993). Back-propagation pattern recognisers for x-bar control charts: methodology and performance. Computers and Industrial Engineering, 24, 219–235. Japkowicz, N. and Shaju, S. (2002). The class imbalance problem: A systematic study. Intelligent Data Analysis, 6(5), 429-450. Johnson, S. (1967). Hierarchical clustering schemes. Psychometrika, 32 (3), 241-254. Kaempf, U. (1995). The binomial test: a simple tool to identify process problems. IEEE Transactions on Semiconductor Manufacturing 8(2), 160-166. Karypis, G., Han, E.H. and Kumar, V. (1999). Chameleon: hierarchical clustering using dynamic modeling. Computer 32 (8), 68-75. Kaufman, L. and Rousseeuw, P. (1990). Finding groups in data: an introduction to cluster analysis. New York: John Wiley and Sons. Kass, G.V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 292(2), 119-127. Keki, B., Cheng, J., Fayyad, U. and Qian, Z. (1993). Applying machine learning to semiconductor manufacturing. IEEE Expert, 41-47. Kittler, R. and Wang, W. (2000). Data mining for yield improvement. In Proceeding of . International Conference of Modeling Analysis of Semiconductor Manufacturing (MASM), 270-277. Kohonen, T., Huang, T.S., and Schroeder, M.R. (2000). Self-organization maps. Springer-Verlag. Koç, M. and Lee, J. (2002), e-Manufacturing and e-Maintenance applications and benefits. International Conference on Responsive Manufacturing (ICRM), 2002, 26–29. Kullback, S. and Leibler, R.A (1951). On information and sufficiency. Annals of Mathematical Statistics, 22, 76-86. Kusiak, A. (2000). Decomposition in data mining: an industrial case study. IEEE Transactions on Electronic Packaging Manufacturing, 23(4), 345-352. Lance, G. and Willians, W. (1967). A general theory of classification sorting strategies. Computer Journal, 9, 373-386. Leachman, R.C. and Hodges, D.A. (1996). Benchmarking semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 9(2), 158-169. Lee, J.H., Yu, S.J., and Park, S.C. (2001a). Design of intelligent data sampling methodology based on data mining. IEEE Transaction on Robotics and Automation, 5(17), 637–649. Lee, J.H., Yu, S.J., and Park, S.C. (2001b). A new intelligent SOFM-based sampling plan for advanced process control. Expert Systems with Applications, 20, 133–151. Lim, T.S., Loh, W.Y., and Shih, Y.S. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40, 203-229. Liu, S.F., Chen, F.L., and Lu, W.B. (2002). Wafer bin map recognition using a neural network approach. International Journal of Production Research, 40, 2207–2223. Liu, C., Chien, C., and Ho. I. (1998). An object-oriented analysis and design method for shop floor control systems. International Journal of Computer Integrated Manufacturing,.11, 379-400. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceeding of 5th Berkeley Symposium, 9-24. Mallory, C.L., Perloff, D.S., Hasan, T.F. and Stanley, R.M. (1983). Spatial yield analysis in integrated circuit manufacturing. Solid State Technology, November, 121-127. Masson, C. and Smith, A. (2006). Architecting the next generation of EMI: operations intelligence meets business intelligence. Article from AMR Research Boston. Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist, 50, 741–749. Miguelanez, E., Zalzala, A.M.S. and Tabor, P. (2004). Evolving neural networks using swarm intelligence for binmap classification. In IEEE world congress on evolutionary computation, Portland, USA, 1, 978-985. Min, H. and Yih, Y. (2007). A data mining approach to production control in dynamic manufacturing system. In Liao, T.W and Triantaphyllou, E. (Eds..), Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications. World Scientific, Singapore, 287-321. Morel, G., Panetto, H., Zaremba, M. and Mayer, F. (2003). Manufacturing enterprise control and management system engineering: paradigms and open issues. Annual Reviews in Control 27 (2), 199-209. Navarro, Julio F., Frenk, Carlos S. and Simon D.M.W. (1997). A universal density profile from hierarchical clustering, Astrophysical Journal, 490-493. Neaga, E.I. and Harding, J. A. (2005). An enterprise modeling and integration framework based on knowledge discovery and data mining, International Journal of Production Research, 43(6), 1089 – 1108. Nylund, A. (1999). Tracing the BI family tree. Knowledge Management, July 1999. Olszak C. and Ziemba, E. (2007). Approach to building and implementing business intelligence systems. Interdisciplinary Journal of Information, Knowledge, and Management, 2, 35-148 Palma, F.D., Nicolao, G.D., Miraglia, G., Pasquinetti, E., and Piccinini, F. (2005). Unsupervised spatial pattern classification of electrical-wafer-sorting maps in semiconductor manufacturing. Pattern Recognition, 26, 1857–1865. Peng, C. and Chien, C. (2003). Data value development to enhance yield and maintain competitive advantage for semiconductor manufacturing. International Journal of Service Technology and Management, 4(6), 365-383. Peng. J., Chien, C. and Tseng, B. (2004). Rough set theory for data mining for fault diagnosis on distribution feeder. IEE Proceedings-Generation, Transmission, and Distributions. 151(6), 689-697. Peng, J., Chang, S., Chien, C. and Yang, J. (2005). Constructing a data mining framework of association rule and an empirical study for fault location. Journal of Information Management, 12(4), 121-141. Perry; M.B., Spoerre, J.K. and Velasco, T. (2001). Control chart pattern recognition using back propagation artificial neural networks. International Journal of Production Research, 39(15), 3399 – 3418. Pham, D.T. and Wani, M.A. (1997). Feature-based control chart pattern recognition. International Journal of Production Research, 35(7), 1875–1890. Porter, M. (1980). Competitive Strategy: techniques for analyzing industries and competitors. The Free Press, New York. Quinlan, J.R. (1993). C4.5: programs for machine learning. Morgan Kaufmann, San Francisco, California. Rising, L. (1998). The patterns handbook: techniques, strategies, and applications. Cambridge University Pr, UK. Rzevski, G. (1997). A framework for designing intelligent manufacturing systems. Computers in Industry, 34(2), 211-219 Sander, J., Ester, M., Kriegel, H.-P. and Xu, X. (1998). Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. In Data Mining and Knowledge Discovery, 2(2), 169-194. Schikuta, E. (1996). Grid-clustering: A hierarchical clustering method for very large data sets. Proceeding of 13th International Conference on Pattern Recognition, 2, 101-105. Schmid, H.A. (1998). Design pattern to construct the hot spots of a manufacturing framework. in the patterns handbook: techniques, strategies, and applications. Cambridge Press, 443-470 Sheikholeslami, G., Chartterjee, S., and Zhang, A. (1998). WaveCluster: A multi-resolution clustering approach for very large spatial databases. In Proceedings of the 24th Conference on VLDB, New York, NY, 428-439. Shetty, D., Motiwalla, L., Kondo, J., Embong, S. and Kathawala, Y. (1996). Real-time architecture for advanced quality monitoring in manufacturing. The International Journal of Quality & Reliability Management, 13(5), 91. Skinner, K.R., Montgomery, D.C., Runger, G.C., Fowler, J.W., McCarville, D.R., Rhoads, T.R. and Stanley, J.D. (2002). Multivariate statistical methods for modeling and analysis of wafer probe test data. IEEE Transactions on Semiconductor Manufacturing, 15(4), 523 – 530. Soukup, T. and Davidson, I. (2002). Visual data mining: techniques and tools for data visualization and mining. John Wiley & Sons. Sprague, R.H., Jr. (1980). A framework for the development of decision support systems. MIS Quarterly, 4(4), 1-26. Stapper, C.H. (2000). LSI yield modeling and process monitoring. IBM Journal of Research and Development, 44(2), 112-118. Streiner DL, Norman GR. (1995). Health Measurement Scales- A practical guide to their development and use. Oxford Medical Publications, Inc. Second Edition. Taam, W. and Hamada, M. (1993). Detecting spatial effects from factorial experiments: an application from integrated-circuit manufacturing. Technometrics 35(2), 149-160. Tan, P., Steinbach, M. and Kumar, V. (2005), Introduction to Data Mining, Addison Wesley. Thomopoulos, S., Bougoulias, D., and Wann, C. (1995). Dignet: an unsupervisedlearning clustering algorithm for clustering and data fusion. IEEE Transaction on Aerospace and Electrical Systems, 31(1), 21-38. Van den Bout, D.E. and Miller, T.K. (1988). Graph partitioning using annealed neural networks. This paper appears in: 1989. IJCNN., International Joint Conference on Neural Networks, Washington, DC, 1, 521-528. Vercellis, C. (2009). Business Intelligence: Data mining and optimization for decision making. John Wiley & Sons Ltd. Wallace, C. and Dowe, D. (1994). Intrinsic classification by MML – the Snob program. In the Proceedings of the 7th Australian Joint Conference on Artificial Intelligence, UNE, World Scientific Publishing Co., Armidale, Australia, 37-44. Wang, C.H. (2009). Separation of composite defect patterns on wafer bin map using support vector clustering. Expert Systems with Applications, 36(2), 2554-2561. Wang, H., Chien, C., Hsu, S., and Lee, P. (2002). A data mining framework and an empirical study of decision tree analysis in semiconductor manufacturing. Journal of Technology Management, 7(1), 137-160. Wang, J., and Spanos, C.J. (2002). Real-Time furnace modeling and diagnostics. IEEE Transactions on Semiconductor Manufacturing, 15(4), 393-403. Wang, W., Yang, J. and Muntz, R. R. (1997). Sting: A statistical information grid approach to spatial data mining. In Proceeding of Twenty-Third International Conference on Very Large Data Bases. Morgan Kaufmann, Athens, Greece, 186-195. Watson, H.J. and Gray P. (2008). What’s new in BI. Journal of Business Intelligence, 13(1), 4-6. Watson, H.J.., Wixom, B.H. (2007). The Current state of business intelligence. Computer, 40(9), 96-99. Werbos, P. J. (1974). Beyond regression: new tools for prediction and analysis in behavioral sciences, Doctoral dissertation. Applied Mathematics, Harvard University, Cambridge, MA. Western Electric Co. (1956). Statistical quality control handbook. (1st ed.) Xu, X., Ester, M., Kriegel, H., and Sander, J. (1998a). A distribution-based clustering algorithm for mining in large spatial databases. In Proceedings of the 14th ICDE, Orlando, FL, 324-331. Xu, X., Ester M., Kriegel H. and Sander J. (1998b). A nonparametric clustering algorithm for knowledge discovery in large spatial databases’, In Proceeding of IEEE International. Conference on Data Engineering, IEEE Computer Society Press. Zachman, J.A. (1987). A framework for information systems architecture. IBM SYSTEMS JOURNAL, 26(3), 276-292. Zhang, T., Ramakrishnan, R. and Livny, M. (1997). Birch: A new data clustering algorithm and its applications. Data Mining and Knowledge Discovery, 1 (2), 141-182. Zeng, H.-J., He, Q.-C., Chen, Z., Ma, W.-Y. and Ma, J. (2004). Learning to cluster web search results. In: Proceedings of the 27th annual international conference on Research and development in information retrieval. ACM Press, 210-217. Zeng , L., Xu, L., Shi, Z. Wang, M. and Wu, W. (2006). Techniques, process, and enterprise solutions of business intelligence, systems, man and cybernetics. SMC '06. IEEE International Conference, 6, 4722-4726
|