52, 1017-1024. Agard, B. and Kusiak, A., (2004a). Data mining based methodology for the design of product families, Int. J. Production Research, 42




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Enterprise Data Mining Bibliography

(updated on December 12, 2007)


Abonyi, J., Feil, B., Nemeth, S., and Arva, P. (2005). Modified Gath-Geva clustering for fuzzy segmentation of multivariate time series, Fuzzy Sets and Systems, 149, 39-56.

Adams, L., (2002). Mining factory data, May 2002, Business News Publishing Company (www.bnp.com).

Adams, N.M., Hand, D. J., and Till, R. J. (2001). Mining for classes and patterns in behavioral data, Journal of the Operational Research Society, 52, 1017-1024.

Agard, B. and Kusiak, A., (2004a). Data mining based methodology for the design of product families, Int. J. Production Research, 42(15), 2955-2969.

Agard, B. and Kusiak, A. (2004b). Data mining for subassembly selection, J. Manufacturing Science and Engineering, 126, 627-631.

Anand, S. S., Patrick, A. R., Hughes, J. G., and Bell, D. A., (1998). A data mining methodology for cross-sales, Knowledge-Based Systems, 10, 449-461.

Anglano, C., Giordana, A., and Bello, G. L. (1999). High-performance data mining on networks of workstations, Proc. 11th Int. Symposium on Intelligent Systems, Warsaw, Poland, June 1999, 520-528.

Apté, C., Weiss, S., Grout, G. (1993). Predicting defects in disk drive manufacturing: a case study in high-dimensional classification, Proc. 9th Conf. Artificial Intelligence on Applications, 212-218.

Au, W.-H., Chan, K. C. C., and Yao, X. (2003). A novel evolutionary data mining algorithm with applications to churn prediction, IEEE Trans. Evolutionary Computation, 7(6), 532-545.

Backus, P., Janakiram, M., Movzoon, S., Runger, G., and Bhargava, A. (2006). Factory cycle-time prediction with a data-mining approach, IEEE Trans. Semiconductor Manufacturing, 19(2), 252-258.

Bae, H., Kim, S., and Woo, K.-B., (2005). Prediction modeling for ingot manufacturing process utilizing data mining roadmap including dynamic polynomial neural network and bootstrap method, L. Wang, K. Chen, and Y. S. Ong (Eds.): ICNC 2005, LNCS 3611, 564-573.

Bae, H., Kim, S., Woo, K.-B., May, G. S., and Lee, D.-K., (2006). Fault detection, diagnosis, and optimization of wafer manufacturing processes utilizing knowledge creation, Int. J. Control, Automation, and Systems, 4(3), 372-381.

Bae, S. M., Ha, S. H.,a nd Park, S. C., (2005). A web-based system for analyzing the voices of call center customers in the service industry, Expert Systems with Applications, 28, 29-41.

Baek, J.-G., Kim, C.-O., and Kim, S. S., (2002). Online learning of the cause-and-effect knowledge of a manufacturing process, Int. J. Prod. Res., 40(14), 3275-3290.

Bansal, K., Vadhavkar, S., Gupta, A. (1998). Neural network based forecasting techniques for inventory control applications, Data Mining and Knowledge Discovery, 2, 97-102.

Bergeret, F. and Le Gall C. (2003). Yield improvement using statistical analysis of process dates, IEEE Trans. Semiconductor Manufacturing, 16(3), 535-542.

Berry, M. J. A. and Linoff, G. (2004). Data Mining Techniques: For Marketing, Sales, and Customer Support, Wiley: New York, NY, U.S.A.

Bertino, E., Catania, B., and Caglio, E. (1999). Applying data mining techniques to wafer manufacturing, Technical Report, University of Milan, Italy, 1999 (http://citeseer.nj.nec.com/context/1457533/377982).

Besse, P. and Le Gall, C. (2005). Application and reliability of change-point analyses for detecting a defective stage in integrated circuit manufacturing (http://www.lsp.upstlse.fr/Recherche/Publications/2005/bes02.pdf).

Black, M. and Hickey, R. (1999). Maintaining the performance of a learned classifier under concept drift, Intelligent Data Analysis, 3, 453-474.

Black, M. and Hickey, R. (2003). Learning classification rules for telecom customer call data under concept drift, Soft Computing, 8, 102-108.

Bolton, R. J. and Hand, D. J. (2002). Statistical fraud detection: a review, Statistical Science, 17(3), 235-255.

Braha, D. (Ed.) (2001). Data Mining for Design and Manufacturing: Methods and Applications, Kluwer: New York, NY, U.S.A.

Braha, D., Elovici, Y., and Last, M. (2007). Theory of actionable data mining with application to semiconductor manufacturing control, Int. J. Production Research, 45(13), 3059-3084.

Braha, D. and Shmilovici, A. (2002). Data mining for improving a cleaning process in the semiconductor industry, IEEE Trans. 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 Trans. Semiconductor Manufacturing, 16(4), 644-652.

Brence, J. R. and Brown, D. E. (2002). Data mining corrosion from eddy current non-destructive tests, Computers & IE, 43, 821-840.

Brijs, T., Swinnen, G., Vanhoof, K., and Wets, G. (2004). Building an association rules framework to improve product assortment decisions, Data Mining and Knowledge Discovery, 8, 7-23.

Buddhakulsomsiri, J., Siradeghyan, Y., Zakarian, A., and Li, X. (2006). Association rule-generation algorithm for mining automotive warranty data, Int. J Production Research, 44(14), 2749-2770.

Büchner, A. G., Anand, S. S., and Hughes, J. G. (1997). Data mining in manufacturing environments: goals, techniques, and applications, Studies in Informatics and Control, 6(4), 319-328.

Burges, J. C. (1998). A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2, 121-167.

Cannataro, M., Congiusta, A., Talia, D., and Trunfio, P. (2002). A data mining toolset for distributed high-performance platforms, Proc. 3rd Int. Conf. Data Mining, Bologna, Italy, September, 41-50.

Cannataro, M. and Talia, D. (2003). The Knowledge Grid, Communications of the ACM, 46(1), 89-93.

Chan, P. K., Fan, W., Prodromidis, a. L., and Stolfo, S. J., (1999). Distributed data mining in credit card fraud detection, IEEE Intelligent Systems, November/December, 67-74.

Chang, P. C., Hsieh, J.C., and Liao, T. W. (2005b). Evolving fuzzy rules for due date assignment problem in semiconductor manufacturing factory,” J. of Intelligent Manufacturing, 16(4-5), 549-557.

Chang, P. C. and Liao, T. W. (2002). Generation of fuzzy due-date assignment rules, FSKD’02, Proc. 1st Int. Conf. on Fuzzy Systems and Knowledge Discovery, Vol. II, Nov. 18-22, 2002, Orchid Country Club, Singapore, 611-615.

Chang, P. C. and Liao, T. W. (2006). Combining SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory,” Applied Soft Computing, 6(2), 198-206.

Chang, P. C., Wang, Y.-W., and Tsai, C.-Y. (2005a). Evolving neural network for printed circuit board sales forecasting, Expert Systems with Applications, 29, 83-92.

Chattratichat, J., Darlington, J., Guo, Y., Hedvall, S., Köhler, M., and Syed, J. (1999). An architecture for distributed enterprise data mining, Lecture Notes of Computer Science 1593, 573-582.

Chen, F.-L. and Liu, S.-F. (2000). A neural-network approach to recognize defect spatial pattern in semiconductor fabrication, IEEE Trans. Semiconductor Manufacturing, 13(3), 366-373.

Chen L.-D., Sakaguchi, T., and Frolick, M. N. (2000a) Data mining methods, applications, and tools, Information Systems Management, Winter 2000, 65-70.

Chen, M.-C., Huang, C.-L., Chen, K.-Y., and Wu, H.-P. (2005). Aggregation of orders in distribution centers using data mining, Expert Systems with Applications, 28, 453-460.

Chen, Q., Dayal, U., and Hsu, M. (2000b). OLAP-based data mining for business intelligence applications in telecommunications and e-commerce, S. Bhalla (Ed.): DNIS 2000, LNCS 1966, 1-19.

Chen, W.-C., Tseng, S.-S., and Wang, C.-Y. (2004). A novel manufacturing defect detection method using data mining approach, IEA/AIE 2004, LNAI 3029, 77-86.

Chien, C.-F., Wang, W.-C., and Cheng, J.-C. (2007). Data mining for yield enhancement in semiconductor manufacturing and an empirical study, Expert Systems with Applications, 33, 192-198.

Chu, Y.-H., Lee, Y.-H., and Han, C. (2004a). Improved quality estimation and knowledge extraction in a batch process by bootstrapping-based generalized variable selection, Ind. Eng. Chem. Res., 43, 1680-1690.

Chu, Y.-H., Qin, S. J., and Han, C. (2004b). Fault detection and operation mode identification based on pattern classification with variable selection, Ind. Eng. Chem. Res., 43, 1701-1710.

Coppola, M., Pesciullesi, P., Ravazzolo, R., and Zoccolo, C. (20204). A parallel knowledge discovery system for customer profiling, M. Danelutto, D. Laforenza, M. Vanneschi (Eds.): Euro-Par 2004, LNCS 3149, 381-390.

Corts, C. and Vapnik, V. N. (1995). Support vector networks, Machine Learning, 20, 273-297.

Cox, L. A., Jr. (2002). Data mining and causal modeling of customer behavior, Telecommunication Systems, 21(2-4), 349-381.

Crespo, F. and Weber, R. (2005). A methodology for dynamic data mining based on fuzzy clustering, Fuzzy Sets and Systems, 150, 267-284.

Cunha, C. D., Agard, B., and Kusiak, A., Data mining for improvement of product quality, Int. J. Production Research, 44(18-19), 4027-4041.

Cunningham, S. P. and MacKinnon, S. (1998). Statistical methods for visual defect metrology, IEEE Trans. Semiconductor Manufacturing, 11(1), 48-53.

Darlington, J., Guo, Y., Sutiwaraphum, J., and To, H. W. (1997). Parallel induction algorithms for data mining, Proc. 2nd Int. Symposium on Intelligent Data Analysis (IDA ’97), London, U. K., August 4-6, 1997, 437-445.

Daskalaki, S., Kopanas, I., Goudara, M., and Avouris, N. (2003). Data mining for decision support on customer insolvency in telecommunications business, European J. of Operational Research, 145, 239-255.

Dengiz, R., Smith, a. E., and Nettleship, I. (2006). Two-stage data mining for flaw identification in ceramics manufacture, Int. J. Production Research, 44(14), 2839-2851.

Fayyad, U. M. and Irani, K. B. (1993). Multi-interval discretization of continuous-valued attributes, Proc. 13th Int’l Joint Conf. Artificial Intelligence, 1993, 1022-1027.

Fayyad, U., Shapiro, G. P., and Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data, Commun. of the ACM, 39, 27-34.

Garcia-Flores, R., Wang, X. Z., and Burfess, T. F. (2003). Tuning inventory policy parameters in a small chemical company, Journal of the Operational Research Society, 54, 350-361.

Garcia-Munos, S., Kourti, T., McGregor, J. F., Mateos, A. G., and Murphy, G. (2003). Troubleshooting of an industrial batch process using multivariate methods, Ind. Eng. Chem. Res., 42, 3592-3601.

Gardner, M. and Bieker, J. (2000). Data mining solves tough semiconductor manufacturing problems, Proc. KDD 2000, Boston, MA, U.S.A., 376-383.

Ge, X. and Smyth, P. (2000). Segmental semi-Markov models for change-point detection with applications to semiconductor manufacturing, Technical Report UCI-ICS 00-08, Department of Information and Computer Science, University of California, Irvine, March 2000.

Gertosio, C. and Dussauchoy, A. (2004). Knowledge discovery from industrial databases, Journal of Intelligent Manufacturing, 15, 29-37.

Gibbons, W.M., Ranta, M., Scott, T. M., and Mantyla, M. (2000). Information management and process improvement using data mining techniques, R. Loganantharaj et al. (Eds.): IEA/AIE 2000, LNAI 1821, 2000, 93-98.

Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination,” Biometrika, 82, 711-732.

Grochowski, M. and Jankowski, N. (2004). Comparison of instances selection algorithms II. Results and comments, ICAISC 2004, LNAI 3070, L. Rutkowski et al. (Eds.), 580-585.

Ha, S. H., (2007). Applying knowledge engineering techniques to customer analysis in the service industry, Advanced Engineering Informatics, 21, 293-301.

Ha, S. H., Bae, S. M., and Park, S. C. (2002). Customer’s time variant purchase behavior and corresponding marketing strategies: an on-line retailer’s case, Computers & IE, 43, 801-820.

Hall, L. O., Chawla, N., Bowyer, K. W., and Kegelmeyer, W. P. (2000). Learning rules from distributed data, Large-Scale Data Mining, LNAI 1750, M. J. Zaki and C.-T. Ho (Eds.), Springer-Verlag, 211-220.

Hall, M. A. and Holmes, G. (2003). Benchmarking attribute selection techniques for discrete class data mining, IEEE Trans. Knowledge and Data Engineering, 15(6), 1437-1447.

Hamuro, Y., Katoh, N., Matsuda, Y., Yada, K. (1998). Mining pharmacy data helps to make profits, Data Mining and Knowledge Discovery, 2, 391-398.

Han, J. and Kamber, M. (2001). Data Mining: Concepts and Techniques, Morgan Kaufmann: San Francisco, CA, U.S.A.

Han, Y., Kim, J., and Lee, C. (2005). Automatic detection of failure patterns using data mining, R. Khosla et al. (Eds.), KES 2005, LNAI 3682, 1312-1316.

Hand, D., Mannila, H., and Smyth, P. (2001). Principles of Data Mining, MIT Press: Cambridge, MA, U.S.A.

Harding, J. A., Shahbaz, M., Srinivas, S., and Kusiak, A. (2006). Data mining in manufacturing: a review, Journal of Manufacturing Science and Engineering, 128, 969-976.

Harrison, P. G. and Lladó, C. M. (2000). Performance evaluation of a distributed enterprise data mining system, TOOLS 2000, LNCS 1786, 2000, 117-131.

Haughton, D., Deichmann, J., Eshghi, A., Sayek, S., Teebagy, N., and Topi, H. (2003). “A review of software packages for data mining,” The American Statistician, 57(4), 290-309.

Ho, G. T. S., Lau, H. C. W., Lee, C. K. M., Ip, A. W. H., and Pun, K. F. (2006). An intelligent production workflow mining system for continual quality enhancement, Int. J. Adv. Manuf. Technol., 28, 792-809.

Hormozi, A. M. and Giles, S. (2004). Data mining: a competitive weapon for banking and retail industries, Information Systems Management, Spring 2004, 62-71.

Hou, T.-H. and Huang, C.-C. (2004). Application of fuzzy logic and variable precision rough set approach in a remote monitoring manufacturing process for diagnosis rule induction, J. of Intelligent Manufacturing, 15, 395-408.

Hsu, C. W. and Lin, C. J. (2002). A comparison of methods for multi-class support vector machines, IEEE Trans. Neural Networks, 13(2), 415-425.

Hsu, C.-H. and Wang, M.-J. J. (2005). Using decision tree based data mining to establish a sizing system for the manufacture of garments, Int. J. Advanced Manufacturing Technology,
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