Abstract
Association rule mining, studied for over ten years in the literature of data mining, aims to help enterprises with sophisticated decision making, but the resulting rules typically cannot be directly applied and require further processing. In this paper, we propose a method for actionable recommendations from itemset analysis and investigate an application of the concepts of association rules—maximal-profit item selection with cross-selling effect (MPIS). This problem is about choosing a subset of items which can give the maximal profit with the consideration of cross-selling effect. A simple approach to this problem is shown to be NP-hard. A new approach is proposed with consideration of the loss rule—a rule similar to the association rule—to model the cross-selling effect. We show that MPIS can be approximated by a quadratic programming problem. We also propose a greedy approach and a genetic algorithm to deal with this problem. Experiments are conducted, which show that our proposed approaches are highly effective and efficient.
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References
Agrawal, R., 2004. IBM Synthetic Data Generator, http://www.almaden.ibm.com/software/quest/Resources/datasets/syndata.html’.
Agrawal, R., Imilienski, T., and Swami. 1993. Mining association rules between sets of items in large databases In Proc. of ACM SIGMOD, pp. 207–216.
Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. In Proc. of 20th VLDB Conference, pp. 487–499.
Beasley, J. 1998. Heuristic algorithms for the unconstrained binary quadratic programming problem. Technical report, the Management School, Imperial College, London.
Blischok, T. 1995. Every transaction tells a story. Chain Store Age Executive with Shopping Center Age 71(3): pp. 50–57.
Brijs, T., Goethals, B., Swinnen, G., Vanhoof, K., and Wets, G. 2000. A data mining framework for optimal product selection in retail supermarket data: The generalized PROFSET Model. Proc. of ACM SIGKDD, pp. 300–304.
Brijs, T., Swinnen, G., Vanhoof, K., and Wets, G. 1999. Using association rules for product assortment decisions: A case study. In Proc. of ACM SIGKDD, pp. 254–260.
Campo, K., Gijsbrechts, E., and Nisol, P. 2003. The impact of retailer stockouts on whether, how much, and what to buy. International Journal of Research in Marketing, 20:273–286.
Cavicchio, D.J. 1970. Adaptive search using simulated evolution. Ph. D. dissertation, Univ. Michigan, Ann Arbor, MI.
Garey, M. and Johnson, D. 1979. Computers and Intractability: A Guide to the Theory of NP-Completeness. USA: Freeman.
Gruen, T., Corsten, D., and Bharadwaj, S. 2002. Retail out-of-stocks: A world-wide examination of extent, causes and consumer responses. Washington D.C.: Grocery Manufacturers of America.
Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. In Proc. of ACM SIGMOD, pp. 1–12.
Hedberg, S. 1995. The data gold rush. In BYTE, pp. 83–99.
Hiller and Lieberman. 2001. Introduction to Operations Research, 7th ed, USA: McGraw Hill.
Hohenbalken, B.V. 1975. A Finite algorithm to maximize certain pseudoconcave functions on polytopes. In Mathematical Programming 8.
Horst, R., Pardalos, P.M., and Thoai, N.V. 2000. Introduction to Global Optimization, 2nd edn. Kluwer Academic Publishers.
Hull, J.C. 1997. Options, Futures, and Other Derivatives, 3rd edn. Prentice Hall International, Inc.
Iasemidis, L., Pardalos, P., Sackellares, J., and Shiau, D. 2001. Quadratic binary programming and dynamical system approach to determine the predictability of epileptic seizures. Journal of Combinatorial Optimization, Kluwer Academic, 5: 9–26.
Kleinberg, J., Papadimitriou, C., and Raghavan, P. 1998. A microeconomic view of data mining. Knowledge Discovery Journal, 2. 311–324.
Kleinberg, J.M. 1998. Also in JACM 46:5, 1999, Authoritative sources in a hyperlinked environment. In Proc. ACM-SIAM Symp. on Discrete Algorithms.
Kok, A. and Fisher, M. 2004. Demand Estimation and Assortment Optimization under Substitution: Methodology and Application. Working paper,Fuqua School of Business, Duke University.
Leon, S.J. 1998. Linear Algebra with Applications, 5th edn. Prentice Hall.
Luo, J., Pattipati, K.R., and Willett, P. 2001. A sub-optimal soft decision PDA method for binary quadratic programming. In Proc. of the IEEE Systems, Man, and Cybernetics Conference, pp. 3140–3145.
Mahfoud, S.W. 1992. Crowding and preselection revisited. Parallel Problem Solving from Nature, 2:27–36.
Mannila, H. 1997. Methods and problems in data mining. In Proc. of Int. Conf. on Database Theory, pp. 41–55.
Mannila, H., Toivonen, H., and Verkamo, A.I. 1994. Efficient algorithms for discovering association rules. In Proc. of KDD, pp. 181–192.
Safronov, V. and Parashar, M. 2002. Optimizing web servers using page rank prefetching for clustered accesses. In World Wide Web: Internet and Web Information Systems V 5(1): 165 –176.
Sahni, S. 1974. Computationally related problems. SIAM J. Comput. 3:262–279.
Syswerda, G. 1989. Uniform crossover in genetic algorithms. In Proc. 3rd International Conference of Genetic Algorithms, pp. 2 –9.
Taylor, B. 2001, Inventory Management. In: Introduction to Management Science, 7th Edition. Prentice Hall, Chapter 16.
Ullman, J. 2003. Lecture Notes at http://www-db.stanford.edu/~ullman/mining/mining.html’.
Urban, T. 1998. An inventory-theoretic approach to product assortment and shelf-space allocation. Journal of Retailing, 74:15–35.
Wang, K. and Su, M. 2002. Item selection by “Hub-Authority,” Profit Ranking. In Proc. of ACM SIGKDD, pp. 652–657.
Wong, R., Fu, A., and Wang, K. 2003. MPIS: maximal-profit item selection with cross-selling considerations. In Proc. of IEEE ICDM, pp. 371–378.
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Wong, R.CW., Fu, A.WC. & Wang, K. Data Mining for Inventory Item Selection with Cross-Selling Considerations. Data Min Knowl Disc 11, 81–112 (2005). https://doi.org/10.1007/s10618-005-1359-6
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DOI: https://doi.org/10.1007/s10618-005-1359-6