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Synergies of Operations Research and Data Mining

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  • Meisel, Stephan
  • Mattfeld, Dirk

Abstract

In this contribution we identify the synergies of Operations Research and Data Mining. Synergies can be achieved by integration of optimization techniques into Data Mining and vice versa. In particular, we define three classes of synergies and illustrate each of them by examples. The classification is based on a generic description of aims, preconditions as well as process models of Operations Research and Data Mining. It serves as a framework for the assessment of approaches at the intersection of the two procedures.

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  • Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    • Handle: RePEc:eee:ejores:v:206:y:2010:i:1:p:1-10
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    1. Bernataviciene, Jolita & Dzemyda, Gintautas & Kurasova, Olga & Marcinkevicius, Virginijus, 2006. "Optimal decisions in combining the SOM with nonlinear projection methods," European Journal of Operational Research, Elsevier, vol. 173(3), pages 729-745, September.
    2. Karasozen, Bulent & Rubinov, Alexander & Weber, Gerhard-Wilhelm, 2006. "Optimization in Data Mining," European Journal of Operational Research, Elsevier, vol. 173(3), pages 701-704, September.
    3. Huyet, A.L., 2006. "Optimization and analysis aid via data-mining for simulated production systems," European Journal of Operational Research, Elsevier, vol. 173(3), pages 827-838, September.
    4. Trafalis, Theodore B. & Gilbert, Robin C., 2006. "Robust classification and regression using support vector machines," European Journal of Operational Research, Elsevier, vol. 173(3), pages 893-909, September.
    5. Deb Campbell & Randy Erdahl & Doug Johnson & Eric Bibelnieks & Michael Haydock & Mark Bullock & Harlan Crowder, 2001. "Optimizing Customer Mail Streams at Fingerhut," Interfaces, INFORMS, vol. 31(1), pages 77-90, February.
    6. Piramuthu, Selwyn, 1996. "Feed-forward neural networks and feature construction with correlation information: an integrated framework," European Journal of Operational Research, Elsevier, vol. 93(2), pages 418-427, September.
    7. Denton, Frank T, 1985. "Data Mining as an Industry," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 124-127, February.
    8. JosÉ Figueira & Salvatore Greco & Matthias Ehrogott, 2005. "Multiple Criteria Decision Analysis: State of the Art Surveys," International Series in Operations Research and Management Science, Springer, number 978-0-387-23081-8, December.
    9. Lee G. Cooper & Giovanni Giuffrida, 2000. "Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results," Management Science, INFORMS, vol. 46(2), pages 249-264, February.
    10. Li, Xiao-Bai & Jacob, Varghese S., 2008. "Adaptive data reduction for large-scale transaction data," European Journal of Operational Research, Elsevier, vol. 188(3), pages 910-924, August.
    11. Chen, Mu-Chen & Wu, Hsiao-Pin, 2005. "An association-based clustering approach to order batching considering customer demand patterns," Omega, Elsevier, vol. 33(4), pages 333-343, August.
    12. P. S. Bradley & Usama M. Fayyad & O. L. Mangasarian, 1999. "Mathematical Programming for Data Mining: Formulations and Challenges," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 217-238, August.
    13. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
    14. Azzag, Hanene & Venturini, Gilles & Oliver, Antoine & Guinot, Christiane, 2007. "A hierarchical ant based clustering algorithm and its use in three real-world applications," European Journal of Operational Research, Elsevier, vol. 179(3), pages 906-922, June.
    15. Saglam, Burcu & Salman, F. Sibel & Sayin, Serpil & Turkay, Metin, 2006. "A mixed-integer programming approach to the clustering problem with an application in customer segmentation," European Journal of Operational Research, Elsevier, vol. 173(3), pages 866-879, September.
    16. Sigurdur Ólafsson & Jaekyung Yang, 2005. "Intelligent Partitioning for Feature Selection," INFORMS Journal on Computing, INFORMS, vol. 17(3), pages 339-355, August.
    17. Chen, Yen-Liang & Hu, Hui-Ling, 2006. "An overlapping cluster algorithm to provide non-exhaustive clustering," European Journal of Operational Research, Elsevier, vol. 173(3), pages 762-780, September.
    18. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
    19. Shen, Ching-Cheng & Chen, Yen-Liang, 2008. "A dynamic-programming algorithm for hierarchical discretization of continuous attributes," European Journal of Operational Research, Elsevier, vol. 184(2), pages 636-651, January.
    20. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    21. Beliakov, Gleb & King, Matthew, 2006. "Density based fuzzy c-means clustering of non-convex patterns," European Journal of Operational Research, Elsevier, vol. 173(3), pages 717-728, September.
    22. R-H Lin, 2009. "Potential use of FP-growth algorithm for identifying competitive suppliers in SCM," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1135-1141, August.
    23. K J Glowacka & R M Henry & J H May, 2009. "A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1056-1068, August.
    24. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, December.
    25. Carrizosa, Emilio & Martin-Barragan, Belen, 2006. "Two-group classification via a biobjective margin maximization model," European Journal of Operational Research, Elsevier, vol. 173(3), pages 746-761, September.
    26. Agafonov, Evgeny & Bargiela, Andrzej & Burke, Edmund & Peytchev, Evtim, 2009. "Mathematical justification of a heuristic for statistical correlation of real-life time series," European Journal of Operational Research, Elsevier, vol. 198(1), pages 275-286, October.
    27. Salvatore Greco & Benedetto Matarazzo & Roman Słowinński, 2005. "Decision Rule Approach," International Series in Operations Research & Management Science, in: Multiple Criteria Decision Analysis: State of the Art Surveys, chapter 0, pages 507-555, Springer.
    28. O. L. Mangasarian, 1965. "Linear and Nonlinear Separation of Patterns by Linear Programming," Operations Research, INFORMS, vol. 13(3), pages 444-452, June.
    29. Basu, Amit, 1998. "Perspectives on operations research in data and knowledge management," European Journal of Operational Research, Elsevier, vol. 111(1), pages 1-14, November.
    30. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
    31. Balaji Padmanabhan & Alexander Tuzhilin, 2003. "On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities," Management Science, INFORMS, vol. 49(10), pages 1327-1343, October.
    32. Meiri, Ronen & Zahavi, Jacob, 2006. "Using simulated annealing to optimize the feature selection problem in marketing applications," European Journal of Operational Research, Elsevier, vol. 171(3), pages 842-858, June.
    33. Hodrea, I.B. & Bot, R.I. & Wanka, G., 2006. "The Rose-Gurewitz-Fox approach applied for patents classification," European Journal of Operational Research, Elsevier, vol. 173(3), pages 815-826, September.
    34. Sawicki, Piotr & Zak, Jacek, 2009. "Technical diagnostic of a fleet of vehicles using rough set theory," European Journal of Operational Research, Elsevier, vol. 193(3), pages 891-903, March.
    35. Kulkarni, Girish & Fathi, Yahya, 2007. "Integer programming models for the q-mode problem," European Journal of Operational Research, Elsevier, vol. 182(2), pages 612-625, October.
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