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IBM Journal of Research and Development
Business Optimization   Volume 51, Number 3/4, 2007
Table of contents: HTMLPDF This article: HTMLPDF   Copyright info

Advances in analytics: Integrating dynamic data mining with simulation optimization - References

 by M. Better,
F. Glover,
and M. Laguna
References

  1. D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, The MIT Press, Cambridge, MA, 2001.
  2. M. Craven and J. Shavlik, “Using Neural Networks for Data Mining,” Future Generation Computer Syst. (special issue on data mining) 13, No. 2/3, 211–229 (1997).
  3. N. Christiani and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, Cambridge, U.K., 2000.
  4. D. Heckerman, “A Tutorial on Learning with Bayesian Networks,” Technical Report MSR TR-95–06, Microsoft Research, 1995; see http://research.microsoft.com/research/pubs/view.aspx?msr_tr_id=MSR-TR-95-06.
  5. X. Bai and R. Padman, “Tabu Search Enhanced Markov Blanket Classifier for High Dimensional Data Sets,” Proceedings of the 9th INFORMS Computing Society, Springer, New York, 2004, pp. 338–354.
  6. M. Better, F. Glover, and M. Samorani, “Multi-Hyperplane Formulations for Classification and Discrimination Analysis” (working paper), University of Colorado, Boulder, CO, 2006; see http://www.opttek.com/white.html.
  7. F. Glover, “Improved Classification and Discrimination by Successive Hyperplane and Multi-Hyperplane Separation” (working paper), University of Colorado, Boulder, CO, 2006; see http://www.opttek.com/white.html.
  8. M. Better, F. Glover, G. Kochenberger, and H. Wang, “A Novel Approach to Classification in Financial Applications,” Proceedings of the AI/DM Workshop of the INFORMS Annual Meeting, 2006; see http://ieweb.uta.edu/vchen/AIDM/AIDM-Better.pdf.
  9. L. J. Heyer, S. Kruglyak, and S. Yooseph, “Exploring Expression Data: Identification and Analysis of Co-Expressed Genes,” Genome Res. 9, No. 11, 1106–1115 (1999).
  10. F. J. Rohlf, “Hierarchical Clustering Using the Minimum Spanning Tree,” Computer J. 6, No. 1, 93–95 (1973).
  11. M. Ng, “A Parallel Tabu Search Heuristic for Clustering Data Sets,” presented at the International Conference on Parallel Processing Workshops (ICPPW'03), Kaohsiung, Taiwan, 2003.
  12. G. Kochenberger, F. Glover, B. Alidaee, and H. Wang, “Clustering of Microarray Data via Clique Partitioning,” J. Combinatorial Optimization 10, No. 1, 77–92 (2005).
  13. M. Barnett, “Modeling and Simulation in Business Process Management,” BP Trends Newsletter, White Papers & Technical Briefs, pp. 1–10; see http://www.bptrends.com.
  14. V. Campos, F. Glover, M. Laguna, and R. Martí, “An Experimental Evaluation of a Scatter Search for the Linear Ordering Problem” (working paper), University of Colorado, Boulder, CO, 1999; available from authors.
  15. V. Campos, M. Laguna, and R. Martí, “Scatter Search for the Linear Ordering Problem,” New Methods in Optimization, D. Corne, M. Dorigo, and F. Glover, Editors, McGraw-Hill, New York, 1999, pp. 331–339.
  16. F. Glover, “A Template for Scatter Search and Path Relinking,” Artificial Evolution, Lecture Notes in Computer Science 1363, J.-K. Hao, E. Lutton, E. Ronald, M. Schoenauer, and D. Snyers, Editors, Springer-Verlag, New York, 1998, pp. 13–54.
  17. F. Glover and M. Laguna, Tabu Search, Kluwer Academic Publishers, New York, 1997.
  18. F. Glover, M. Laguna, and R. Marti, “Fundamentals of Scatter Search and Path Relinking,” Control and Cybernet. 29, No. 3, 653–684 (2000).
  19. F. Glover, M. Laguna, and R. Marti, Scatter Search, Advances in Evolutionary Computing: Theory and Applications, Springer-Verlag, New York, 2003, pp. 519–537.
  20. M. Laguna, “Scatter Search,” Handbook of Applied Optimization, P. M. Pardalos and M. G. C. Resende, Editors, Oxford University Press, New York, 2002.
  21. OptTek Systems, Inc., Optquest Engine Manual, (available online); see http://www.OptTek.com.
  22. OptTek Systems, Inc., J. April, F. Glover, and J. P. Kelly, “OptFolio®—A Simulation Optimization System for Project Portfolio Planning,” Proceedings of the 2003 Winter Simulation Conference, S. Chick, P. J. Sánchez, D. Ferrin, and D. J. Morrice, Editors, New Orleans, LA, 2003; see http://www.opttek.com/publications/wsc03Final-kellyj85794i.pdf.
  23. J. April, “OptForce™: New Human Resource Optimization Methods,” presented at the INFORMS Conference on O.R. Practice, Miami, FL, April 30–May 2, 2006.
  24. V. Piana, “Consumer Decision Rules for Agent-Based Models,” Economics Web Institute, 2004; see www.economicswebinstitute.org/essays/consumers.htm.
  25. Y. Fu, R. Piplani, R. de Souza, and J. Wu, “Multi-Agent Enabled Modeling and Simulation Towards Collaborative Inventory Management in Supply Chains,” Proceedings of the 2000 Winter Simulation Conference, J. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick, Editors, 2000, pp. 1763–1771.
  26. P. Bilkstein and U. Wilenski, “MaterialSim: An Agent-Based Simulation Toolkit for Learning Materials Science,” presented at the International Conference on Engineering Education, University of Florida, Gainesville, FL, 2004.
  27. C. Langton, Artificial Life: An Overview, MIT Press, Cambridge, MA, 1995.
  28. D. A. Samuelson and C. M. Macal, “Agent-Based Simulation Comes of Age,” ORMS Today 33, No. 4, 34–38 (2006).
  29. K. J. Lancaster, “A New Approach to Consumer Theory,” J. Political Econ. 74, No. 2, 132–157 (1996).


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