Abstract
In Marketing, the goal is to understand the psychology of the customer in order to maximize sales. A common approach is to combine web semantic, sniffing, historical information of the customer, and machine learning techniques.
In this paper, we exploit the historical information of sales in order to assist product placement. The rationale is simple: if two items are sold jointly, they should be close. This concept is formalized in a combinatorial optimization problem, called Max Cut-Clique or \( MCC \) for short.
The hardness of the \( MCC \) promotes the development of heuristics. The literature offers a GRASP/VND methodology as well as an Iterated Local Search (ILS) implementation. In this work, a novel Genetic Algorithm is proposed to deal with the \( MCC \). A comparison with respect to previous heuristics reveals that our proposal is competitive with state-of-the-art solutions.
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Notes
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References
Aguinis, H., Forcum, L.E., Joo, H.: Using market basket analysis in management research. J. Manag. 39(7), 1799–1824 (2013)
Amuthan, A., Deepa Thilak, K.: Survey on Tabu Search meta-heuristic optimization. In: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pp. 1539–1543, October 2016
Bader, G.D., Hogue, C.W.V.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinf. 4, 2 (2003)
Bourel, M., Canale, E.A., Robledo, F., Romero, P., Stábile, L.: A GRASP/VND heuristic for the max cut-clique problem. In: Nicosia, G., Pardalos, P.M., Giuffrida, G., Umeton, R., Sciacca, V. (eds.) Machine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Volterra, Italy, 13–16 September 2018, Revised Selected Papers, LNCS, vol. 11331, pp. 357–367. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-13709-0_30
Brohée, S., van Helden, J.: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinf. 7(1), 488 (2006)
Bruinsma, G., Bernasco, W.: Criminal groups and transnational illegal markets. Crime Law Soc. Change 41(1), 79–94 (2004)
Cascio, W.F., Aguinis, H.: Research in industrial and organizational psychology from 1963 to 2007: changes, choices, and trends. J. Appl. Psychol. 93(5), 1062–1081 (2008)
Cook, S.A.: The complexity of theorem-proving procedures. In: Proceedings of the Third Annual ACM Symposium on Theory of Computing, STOC 1971, pp. 151–158. ACM, New York (1971)
Fagundez, G.: The Malva Project. A framework of artificial intelligence EN C++. GitHub Inc. https://themalvaproject.github.io
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, New York (1979)
Gendreau, M., Potvin, J.-Y.: Handbook of Metaheuristics, 2nd edn. Springer Publishing Company Incorporated (2010)
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Norwell (1997)
Gouveia, L., Martins, P.: Solving the maximum edge-weight clique problem in sparse graphs with compact formulations. EURO J. Comput. Optim. 3(1), 1–30 (2015)
Harary, F.: Graph Theory. Addison Wesley Series in Mathematics. Addison-Wesley (1971)
Henzinger, M., Lawrence, S.: Extracting knowledge from the world wide web. Proc. Nat. Acad. Sci. 101(suppl 1), 5186–5191 (2004)
Holland, J.H.: Adaption in natural and artificial systems (1975)
Hüffner, F., Komusiewicz, C., Moser, H., Niedermeier, R.: Enumerating isolated cliques in synthetic and financial networks. In: Yang, B., Du, D.-Z., Wang, C.A. (eds.) COCOA 2008. LNCS, vol. 5165, pp. 405–416. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85097-7_38
Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum Press (1972). https://doi.org/10.1007/978-1-4684-2001-2_9
Martins, P.: Cliques with maximum/minimum edge neighborhood and neighborhood density. Comput. Oper. Res. 39, 594–608 (2012)
Martins, P., Ladrón, A., Ramalhinho, H.: Maximum cut-clique problem: ILS heuristics and a data analysis application. Int. Trans. Oper. Res. 22(5), 775–809 (2014)
Reeves, C.R.: Genetic algorithms. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, International Series in Operations Research & Management Science, vol. 57, pp. 109–139. Springer, Boston (2010). https://doi.org/10.1007/0-306-48056-5_3
Resende, M.G.C., Ribeiro, C.C.: Optimization by GRASP - Greedy Randomized Adaptive Search Procedures. Computational Science and Engineering. Springer, New York (2016). https://doi.org/10.1007/978-1-4939-6530-4
Slowik, A., Kwasnicka, H.: Evolutionary algorithms and their applications to engineering problems. Neural Comput. Appl. 32, 12363–12379 (2020)
Acknowledgements
This work is partially supported by MATHAMSUD 19-MATH-03 Raredep, Rare events analysis in multi-component systems with dependent components, STICAMSUD 19-STIC-01 ACCON, Algorithms for the capacity crunch problem in optical networks and Fondo Clemente Estable Teoría y Construcción de Redes de Máxima Confiabilidad.
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Fortez, G., Robledo, F., Romero, P., Viera, O. (2020). A Fast Genetic Algorithm for the Max Cut-Clique Problem. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_47
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