Propuesta para múltiples diseños de tiendas basados en reglas de asociación

ilustraciones, diagramas

Autores:: Arboleda Correa, Andrés Felipe

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Propuesta para múltiples diseños de tiendas basados en reglas de asociación
dc.title.translated.eng.fl_str_mv	Proposal for multiple store layouts based on association rules
title	Propuesta para múltiples diseños de tiendas basados en reglas de asociación
spellingShingle	Propuesta para múltiples diseños de tiendas basados en reglas de asociación 000 - Ciencias de la computación, información y obras generales 650 - Gerencia y servicios auxiliares::658 - Gerencia general Comportamiento del consumidor Consumer behavior Minería de datos Análisis de cestas de compra Reglas de asociación Diseño de tiendas Data mining Market basket analysis Association rules Store design
title_short	Propuesta para múltiples diseños de tiendas basados en reglas de asociación
title_full	Propuesta para múltiples diseños de tiendas basados en reglas de asociación
title_fullStr	Propuesta para múltiples diseños de tiendas basados en reglas de asociación
title_full_unstemmed	Propuesta para múltiples diseños de tiendas basados en reglas de asociación
title_sort	Propuesta para múltiples diseños de tiendas basados en reglas de asociación
dc.creator.fl_str_mv	Arboleda Correa, Andrés Felipe
dc.contributor.advisor.none.fl_str_mv	Moreno Arboleda, Francisco Javier
dc.contributor.author.none.fl_str_mv	Arboleda Correa, Andrés Felipe
dc.contributor.orcid.spa.fl_str_mv	Moreno Arboleda, Francisco Javier [0000-0001-7806-6278]
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales 650 - Gerencia y servicios auxiliares::658 - Gerencia general
topic	000 - Ciencias de la computación, información y obras generales 650 - Gerencia y servicios auxiliares::658 - Gerencia general Comportamiento del consumidor Consumer behavior Minería de datos Análisis de cestas de compra Reglas de asociación Diseño de tiendas Data mining Market basket analysis Association rules Store design
dc.subject.lemb.spa.fl_str_mv	Comportamiento del consumidor
dc.subject.lemb.eng.fl_str_mv	Consumer behavior
dc.subject.proposal.spa.fl_str_mv	Minería de datos Análisis de cestas de compra Reglas de asociación Diseño de tiendas
dc.subject.proposal.eng.fl_str_mv	Data mining Market basket analysis Association rules Store design
description	ilustraciones, diagramas
publishDate	2022
dc.date.issued.none.fl_str_mv	2022-11-01
dc.date.accessioned.none.fl_str_mv	2023-01-27T21:10:01Z
dc.date.available.none.fl_str_mv	2023-01-27T21:10:01Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
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status_str	acceptedVersion
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/83174 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	[1] I. Cil, “Consumption universes based supermarket layout through association rule mining and multidimensional scaling,” Expert Syst Appl, vol. 39, no. 10, pp. 8611–8625, Aug. 2012, doi: 10.1016/j.eswa.2012.01.192. [2] A. Borges, Chairholder, and Auchan, “Toward a new supermarket layout : from industrial categories to one stop shopping organization through a data mining approach,” 2004. [3] Y. F. Wang, Y. L. Chuang, M. H. Hsu, and H. C. Keh, “A personalized recommender system for the cosmetic business,” Expert Syst Appl, vol. 26, no. 3, pp. 427–434, Apr. 2004, doi: 10.1016/J.ESWA.2003.10.001. [4] M. C. Chen, “Ranking discovered rules from data mining with multiple criteria by data envelopment analysis,” Expert Syst Appl, vol. 33, no. 4, pp. 1110–1116, Nov. 2007, doi: 10.1016/J.ESWA.2006.08.007. [5] Alex. Berson, S. Smith, and Kurt. Thearling, “Building data mining applications for CRM,” p. 510, 2000. [6] R. Agrawal, T. Imieliński, and A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” in Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 1993, pp. 207–216. doi: 10.1145/170035.170072. [7] P. D. McNicholas, T. B. Murphy, and M. O’Regan, “Standardising the lift of an association rule,” Comput Stat Data Anal, vol. 52, no. 10, pp. 4712–4721, Jun. 2008, doi: 10.1016/J.CSDA.2008.03.013. [8] B. A. Kitchenham and S. Charters, “Guidelines for performing Systematic Literature Reviews in Software Engineering,” Jun. 2007. [Online]. Available: https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf [9] K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic Mapping Studies in Software Engineering,” in Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, 2008, pp. 68–77. [10] A. Adhikari and P. R. Rao, “Association rules induced by item and quantity purchased,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, vol. 4947 LNCS, pp. 478–485. doi: 10.1007/978-3-540-78568-2_37. [11] F. Alfiah et al., “Data Mining Systems to Determine Sales Trends and Quantity Forecast Using Association Rule and CRISP-DM Method,” International Journal of Engineering and Techniques, vol. 4, Accessed: Oct. 22, 2020. [Online]. Available: http://www.ijetjournal.org [12] P. Kumar and & Ananthanarayana, “Discovery of Frequent Itemsets Based on Minimum Quantity and Support,” 2009. Accessed: Oct. 22, 2020. [Online]. Available: https://www.cscjournals.org/library/manuscriptinfo.php?mc=IJCSS-86 [13] J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” 2000, pp. 1–12. doi: 10.1145/342009.335372. [14] J. Han, J. Pei, Y. Yin, and R. Mao, “Mining frequent patterns without candidate generation: A frequent-pattern tree approach,” Data Min Knowl Discov, vol. 8, no. 1, pp. 53–87, Jan. 2004, doi: 10.1023/B:DAMI.0000005258.31418.83. [15] S. Ibrahim and J. Revathy, “A Novel Quantity based Weighted Association Rule Mining,” International Journal of Engineering Inventions, vol. 4, no. 3, Aug. 2014. [16] M. S. Khan, M. Muyeba, and F. Coenen, “A weighted utility framework for mining association rules,” in Proceedings - EMS 2008, European Modelling Symposium, 2nd UKSim European Symposium on Computer Modelling and Simulation, 2008, pp. 87–92. doi: 10.1109/EMS.2008.73. [17] P. S. Sandhu, D. S. Dhaliwal, and S. N. Panda, “Mining utility-oriented association rules: An efficient approach based on profit and quantity,” International Journal of the Physical Sciences, vol. 6, no. 2, pp. 301–307, 2011, doi: 10.5897/IJPS09.303. [18] S. Halim, T. Octavia, and C. Alianto, “Designing facility layout of an amusement arcade using market basket analysis,” in Procedia Computer Science, Jan. 2019, vol. 161, pp. 623–629. doi: 10.1016/j.procs.2019.11.165. [19] “Top 10 FEC & Arcade Game Room Layout Tips \| laigames.com.” https://laigames.com/top-10-fec-arcade-game-room-layout-tips/ (accessed Nov. 12, 2020). [20] Y. L. Chen, K. Tang, R. J. Shen, and Y. H. Hu, “Market basket analysis in a multiple store environment,” Decis Support Syst, vol. 40, no. 2, pp. 339–354, Aug. 2005, doi: 10.1016/j.dss.2004.04.009. [21] L. M. Charlet Annie and A. D. Kumar, “Market Basket Analysis for a Supermarket based on Frequent Itemset Mining,” 2012. Accessed: Oct. 23, 2020. [Online]. Available: www.IJCSI.org [22] L. C. Annie and D. Ashok Kumar, “Frequent Item set mining for Market Basket Data using K-Apriori algorithm,” International Journal of Computational Intelligence and Informatics, vol. 1, no. 1, pp. 14–18, 2011. [23] M. Ali Alan and A. R. Ince, “Use of Association Rule Mining within the Framework of a Customer-Oriented Approach,” European Scientific Journal, ESJ, vol. 12, no. 9, p. 81, Mar. 2016, doi: 10.19044/esj.2016.v12n9p81. [24] G. R. Peterson, “ECONOMICS IN STORE LAYOUT AND DESIGN.” 1970. Accessed: Nov. 12, 2020. [Online]. Available: https://agris.fao.org/agris-search/search.do?recordID=US2012209216 [25] J. S. Larson, E. T. Bradlow, and P. S. Fader, “An exploratory look at supermarket shopping paths,” International Journal of Research in Marketing, vol. 22, no. 4, pp. 395–414, Dec. 2005, doi: 10.1016/j.ijresmar.2005.09.005. [26] J. Cisewski, “Multivariate Analysis, Clustering, and Classification”. [27] S. Altuntas, “A novel approach based on utility mining for store layout: A case study in a supermarket,” Industrial Management and Data Systems, vol. 117, no. 2, pp. 304–319, 2017, doi: 10.1108/IMDS-01-2016-0040. [28] S. Altuntas and H. Selim, “Facility layout using weighted association rule-based data mining algorithms: Evaluation with simulation,” Expert Syst Appl, vol. 39, no. 1, pp. 3–13, Jan. 2012, doi: 10.1016/j.eswa.2011.06.045. [29] R. Z. Farahani, M. SteadieSeifi, and N. Asgari, “Multiple criteria facility location problems: A survey,” Applied Mathematical Modelling, vol. 34, no. 7. Elsevier, pp. 1689–1709, Jul. 01, 2010. doi: 10.1016/j.apm.2009.10.005. [30] F. Yener and H. R. Yazgan, “Optimal warehouse design: Literature review and case study application,” Comput Ind Eng, vol. 129, pp. 1–13, Mar. 2019, doi: 10.1016/j.cie.2019.01.006. [31] J. C. H. Pan, P. H. Shih, M. H. Wu, and J. H. Lin, “A storage assignment heuristic method based on genetic algorithm for a pick-and-pass warehousing system,” Comput Ind Eng, vol. 81, pp. 1–13, Mar. 2015, doi: 10.1016/j.cie.2014.12.010. [32] H. Zhang et al., “Layout design for intelligent warehouse by evolution with fitness approximation,” IEEE Access, vol. 7, pp. 166310–166317, 2019, doi: 10.1109/ACCESS.2019.2953486. [33] T. Likhouzova and Y. Demianova, “Robot path optimization in warehouse management system,” Evol Intell, pp. 1–7, May 2021, doi: 10.1007/s12065-021-00614-w. [34] H. Y. Lee and C. C. Murray, “Robotics in order picking: evaluating warehouse layouts for pick, place, and transport vehicle routing systems,” Int J Prod Res, vol. 57, no. 18, pp. 5821–5841, 2019, doi: 10.1080/00207543.2018.1552031. [35] Department of Economic and Social Affairs, “Central Product Classification (CPC) Version 2.1,” New York, 2015. Accessed: Jun. 21, 2021. [Online]. Available: https://unstats.un.org/unsd/classifications/Econ/Download/In%20Text/CPCv2.1_complete(PDF)_English.pdf [36] K. Buza, A. Buza, and P. B. Kis, “Towards better modeling of supermarkets,” in ICCC-CONTI 2010 - IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics, Proceedings, 2010, pp. 499–503. doi: 10.1109/ICCCYB.2010.5491220. [37] J. Han, M. Kamber, F. Berzal, and N. Marín, “Data Mining: Concepts and Techniques,” vol. 500, 2001. [38] K. Garg, “Mining Efficient Association Rules Through Apriori Algorithm Using Attributes and Comparative Analysis of Various Association Rule Algorithms,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 6, p. 2277, 2013, Accessed: Mar. 21, 2022. [Online]. Available: www.ijarcsse.com [39] M. J. Zaki and M. Ogihara, “Theoretical Foundations of Association Rules,” 2004, Accessed: Jan. 30, 2022. [Online]. Available: https://www.researchgate.net/publication/2921443 [40] F. Liang, H. Li, W. Zhang, and C. Zhang, “An Improved Distance Metric Clustering Algorithm for Association Rules,” J Phys Conf Ser, vol. 1284, no. 1, p. 012030, Aug. 2019, doi: 10.1088/1742-6596/1284/1/012030. [41] N. Hussein, A. Alashqur, and B. Sowan, “Using the interestingness measure lift to generate association rules,” Journal of Advanced Computer Science & Technology, vol. 4, no. 1, pp. 156–162, 2015, doi: 10.14419/jacst.v4i1.4398. [42] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules”. [43] C. Aflori and M. Craus, “Grid implementation of the Apriori algorithm,” Advances in Engineering Software, vol. 38, no. 5, pp. 295–300, 2007, doi: 10.1016/J.ADVENGSOFT.2006.08.011. [44] F. J. M. Arboleda, G. P. Ortega, and J. A. G. Luna, “Temporal Visual Profiling of Market Basket Analysis,” IAENG Int J Comput Sci, vol. 49, no. 2, 2022. [45] J. Leskovec, A. Rajaraman, and J. D. Ullman, “Clustering,” Mining of Massive Datasets, pp. 228–266, Dec. 2014, doi: 10.1017/CBO9781139924801.008. [46] S. Theodoridis and K. Koutroumbas, “Chapter 13 - Clustering Algorithms II: Hierarchical Algorithms,” in Pattern Recognition (Fourth Edition), Fourth Edition., S. Theodoridis and K. Koutroumbas, Eds. Boston: Academic Press, 2009, pp. 653–700. doi: https://doi.org/10.1016/B978-1-59749-272-0.50015-3. [47] I. Kononenko and M. Kukar, “Cluster Analysis,” Machine Learning and Data Mining, pp. 321–358, Jan. 2007, doi: 10.1533/9780857099440.321. [48] The Pennsylvania State University, “Hierarchical Clustering.” https://online.stat.psu.edu/stat555/node/85/ (accessed Mar. 27, 2022). [49] “Agglomerative Hierarchical Clustering,” The Pennsylvania State University. https://online.stat.psu.edu/stat505/lesson/14/14.4 (accessed Mar. 28, 2022). [50] “Hierarchical Clustering.” https://www.solver.com/xlminer/help/hierarchical-clustering-intro (accessed May 01, 2022). [51] C. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. Cambridge University Press, 2008. [52] “Groceries Market Basket Dataset \| Kaggle.” https://www.kaggle.com/datasets/irfanasrullah/groceries (accessed Oct. 14, 2022). [53] A. F. Arboleda and F. Moreno, “Análisis y oportunidades para el diseño de supermercados basado en reglas de asociación,” in Computación para el Desarrollo – XIV Congreso, 2021, pp. 57–67.
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dc.format.extent.spa.fl_str_mv	xvi, 93 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Analítica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Atribución-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Moreno Arboleda, Francisco Javierd1fe92c3ac167b7f99958ab50ff471cc600Arboleda Correa, Andrés Felipe7a47e8ac2861a99750211e6e689cfc6aMoreno Arboleda, Francisco Javier [0000-0001-7806-6278]2023-01-27T21:10:01Z2023-01-27T21:10:01Z2022-11-01https://repositorio.unal.edu.co/handle/unal/83174Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEl éxito de una tienda depende de su capacidad para comprender el comportamiento de los clientes y su capacidad para reaccionar ante cambios en los hábitos de consumo. El análisis de datos sobre las compras que hacen los clientes es clave para buscar nuevas oportunidades de ventas. En este trabajo, se presenta el estado del arte basado en las directrices para la revisión y clasificación sistemática de la literatura, y se propone un algoritmo para la generación de múltiples diseños de las secciones de una tienda en la cual se considera: i) la minería de reglas de asociación, ii) el número total de unidades compradas de los productos en las transacciones en la generación de las reglas (aspecto que suele ser ignorado en las reglas de asociación clásicas), iii) una estructura jerárquica para la clasificación de productos desarrollada por el Departamento de Asuntos Económicos y Sociales de las Naciones Unidas, iv) la selección de reglas de asociación interesantes (que cumplen ciertos umbrales) y v) un conjunto de restricciones que establecen que ciertos productos o categorías de productos no deben estar cercanos en una sección de la tienda. Finalmente, se presenta un experimento con la generación de diseños para el cual se aplica el algoritmo propuesto sobre un conjunto de datos públicos de ventas de un supermercado con el fin de ver la viabilidad de la propuesta. (Texto tomado de la fuente)The success of a store depends on its ability to understand the behavior of its customers and its ability to react to changes in consumer habits. The analysis of customer purchases is key to finding new sales opportunities. In this paper, we present the state of the art based on the guidelines for systematic literature review and classification, and propose an algorithm for the generation of multiple layouts of the sections for a store in which we consider: (i) association rule mining, (ii) the total number of units purchased of the products in the transactions in the generation of the rules (an aspect that is usually ignored by classical association rules), (iii) a hierarchical structure for product classification developed by the United Nations Department of Economic and Social Affairs, (iv) the selection of interesting association rules (that meet certain thresholds), and (v) a set of constraints that state that certain products or categories of products should not be placed close in a section of a store. Finally, an experiment with the generation of designs is presented by applying the algorithm on a set of public supermarket sales to check the feasibility of the proposal.MaestríaMagíster en Ingeniería - AnalíticaÁrea Curricular de Ingeniería de Sistemas e Informáticaxvi, 93 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales650 - Gerencia y servicios auxiliares::658 - Gerencia generalComportamiento del consumidorConsumer behaviorMinería de datosAnálisis de cestas de compraReglas de asociaciónDiseño de tiendasData miningMarket basket analysisAssociation rulesStore designPropuesta para múltiples diseños de tiendas basados en reglas de asociaciónProposal for multiple store layouts based on association rulesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[1] I. Cil, “Consumption universes based supermarket layout through association rule mining and multidimensional scaling,” Expert Syst Appl, vol. 39, no. 10, pp. 8611–8625, Aug. 2012, doi: 10.1016/j.eswa.2012.01.192.[2] A. Borges, Chairholder, and Auchan, “Toward a new supermarket layout : from industrial categories to one stop shopping organization through a data mining approach,” 2004.[3] Y. F. Wang, Y. L. Chuang, M. H. Hsu, and H. C. Keh, “A personalized recommender system for the cosmetic business,” Expert Syst Appl, vol. 26, no. 3, pp. 427–434, Apr. 2004, doi: 10.1016/J.ESWA.2003.10.001.[4] M. C. Chen, “Ranking discovered rules from data mining with multiple criteria by data envelopment analysis,” Expert Syst Appl, vol. 33, no. 4, pp. 1110–1116, Nov. 2007, doi: 10.1016/J.ESWA.2006.08.007.[5] Alex. Berson, S. Smith, and Kurt. Thearling, “Building data mining applications for CRM,” p. 510, 2000.[6] R. Agrawal, T. Imieliński, and A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” in Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 1993, pp. 207–216. doi: 10.1145/170035.170072.[7] P. D. McNicholas, T. B. Murphy, and M. O’Regan, “Standardising the lift of an association rule,” Comput Stat Data Anal, vol. 52, no. 10, pp. 4712–4721, Jun. 2008, doi: 10.1016/J.CSDA.2008.03.013.[8] B. A. Kitchenham and S. Charters, “Guidelines for performing Systematic Literature Reviews in Software Engineering,” Jun. 2007. [Online]. Available: https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf[9] K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic Mapping Studies in Software Engineering,” in Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, 2008, pp. 68–77.[10] A. Adhikari and P. R. Rao, “Association rules induced by item and quantity purchased,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, vol. 4947 LNCS, pp. 478–485. doi: 10.1007/978-3-540-78568-2_37.[11] F. Alfiah et al., “Data Mining Systems to Determine Sales Trends and Quantity Forecast Using Association Rule and CRISP-DM Method,” International Journal of Engineering and Techniques, vol. 4, Accessed: Oct. 22, 2020. [Online]. Available: http://www.ijetjournal.org[12] P. Kumar and & Ananthanarayana, “Discovery of Frequent Itemsets Based on Minimum Quantity and Support,” 2009. Accessed: Oct. 22, 2020. [Online]. Available: https://www.cscjournals.org/library/manuscriptinfo.php?mc=IJCSS-86[13] J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” 2000, pp. 1–12. doi: 10.1145/342009.335372.[14] J. Han, J. Pei, Y. Yin, and R. Mao, “Mining frequent patterns without candidate generation: A frequent-pattern tree approach,” Data Min Knowl Discov, vol. 8, no. 1, pp. 53–87, Jan. 2004, doi: 10.1023/B:DAMI.0000005258.31418.83.[15] S. Ibrahim and J. Revathy, “A Novel Quantity based Weighted Association Rule Mining,” International Journal of Engineering Inventions, vol. 4, no. 3, Aug. 2014.[16] M. S. Khan, M. Muyeba, and F. Coenen, “A weighted utility framework for mining association rules,” in Proceedings - EMS 2008, European Modelling Symposium, 2nd UKSim European Symposium on Computer Modelling and Simulation, 2008, pp. 87–92. doi: 10.1109/EMS.2008.73.[17] P. S. Sandhu, D. S. Dhaliwal, and S. N. Panda, “Mining utility-oriented association rules: An efficient approach based on profit and quantity,” International Journal of the Physical Sciences, vol. 6, no. 2, pp. 301–307, 2011, doi: 10.5897/IJPS09.303.[18] S. Halim, T. Octavia, and C. Alianto, “Designing facility layout of an amusement arcade using market basket analysis,” in Procedia Computer Science, Jan. 2019, vol. 161, pp. 623–629. doi: 10.1016/j.procs.2019.11.165.[19] “Top 10 FEC & Arcade Game Room Layout Tips \| laigames.com.” https://laigames.com/top-10-fec-arcade-game-room-layout-tips/ (accessed Nov. 12, 2020).[20] Y. L. Chen, K. Tang, R. J. Shen, and Y. H. Hu, “Market basket analysis in a multiple store environment,” Decis Support Syst, vol. 40, no. 2, pp. 339–354, Aug. 2005, doi: 10.1016/j.dss.2004.04.009.[21] L. M. Charlet Annie and A. D. Kumar, “Market Basket Analysis for a Supermarket based on Frequent Itemset Mining,” 2012. Accessed: Oct. 23, 2020. [Online]. Available: www.IJCSI.org[22] L. C. Annie and D. Ashok Kumar, “Frequent Item set mining for Market Basket Data using K-Apriori algorithm,” International Journal of Computational Intelligence and Informatics, vol. 1, no. 1, pp. 14–18, 2011.[23] M. Ali Alan and A. R. 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Moreno, “Análisis y oportunidades para el diseño de supermercados basado en reglas de asociación,” in Computación para el Desarrollo – XIV Congreso, 2021, pp. 57–67.EstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83174/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1216717923.2022.pdf1216717923.2022.pdfTesis de Maestría en Ingeniería - Analíticaapplication/pdf1967912https://repositorio.unal.edu.co/bitstream/unal/83174/2/1216717923.2022.pdf0b5ec00818071949768129021f835744MD52THUMBNAIL1216717923.2022.pdf.jpg1216717923.2022.pdf.jpgGenerated Thumbnailimage/jpeg4650https://repositorio.unal.edu.co/bitstream/unal/83174/3/1216717923.2022.pdf.jpg5bf5d0c48d21299516fa1eda896284d9MD53unal/83174oai:repositorio.unal.edu.co:unal/831742024-08-17 00:00:09.596Repositorio Institucional Universidad Nacional de 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