Propuesta para múltiples diseños de tiendas basados en reglas de asociación

dc.contributor.advisorMoreno Arboleda, Francisco Javier
dc.contributor.authorArboleda Correa, Andrés Felipe
dc.contributor.orcidMoreno Arboleda, Francisco Javier [0000-0001-7806-6278]spa
dc.date.accessioned2023-01-27T21:10:01Z
dc.date.available2023-01-27T21:10:01Z
dc.date.issued2022-11-01
dc.descriptionilustraciones, diagramasspa
dc.description.abstractEl 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)spa
dc.description.abstractThe 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.eng
dc.description.curricularareaÁrea Curricular de Ingeniería de Sistemas e Informáticaspa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Analíticaspa
dc.format.extentxvi, 93 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/83174
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellínspa
dc.publisher.facultyFacultad de Minasspa
dc.publisher.placeMedellín, Colombiaspa
dc.publisher.programMedellín - Minas - Maestría en Ingeniería - Analíticaspa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[5] Alex. Berson, S. Smith, and Kurt. Thearling, “Building data mining applications for CRM,” p. 510, 2000.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.pdfspa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.orgspa
dc.relation.references[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-86spa
dc.relation.references[13] J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” 2000, pp. 1–12. doi: 10.1145/342009.335372.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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).spa
dc.relation.references[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.spa
dc.relation.references[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.orgspa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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=US2012209216spa
dc.relation.references[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.spa
dc.relation.references[26] J. Cisewski, “Multivariate Analysis, Clustering, and Classification”.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[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.pdfspa
dc.relation.references[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.spa
dc.relation.references[37] J. Han, M. Kamber, F. Berzal, and N. Marín, “Data Mining: Concepts and Techniques,” vol. 500, 2001.spa
dc.relation.references[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.comspa
dc.relation.references[39] M. J. Zaki and M. Ogihara, “Theoretical Foundations of Association Rules,” 2004, Accessed: Jan. 30, 2022. [Online]. Available: https://www.researchgate.net/publication/2921443spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[42] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules”.spa
dc.relation.references[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.spa
dc.relation.references[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.spa
dc.relation.references[45] J. Leskovec, A. Rajaraman, and J. D. Ullman, “Clustering,” Mining of Massive Datasets, pp. 228–266, Dec. 2014, doi: 10.1017/CBO9781139924801.008.spa
dc.relation.references[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.spa
dc.relation.references[47] I. Kononenko and M. Kukar, “Cluster Analysis,” Machine Learning and Data Mining, pp. 321–358, Jan. 2007, doi: 10.1533/9780857099440.321.spa
dc.relation.references[48] The Pennsylvania State University, “Hierarchical Clustering.” https://online.stat.psu.edu/stat555/node/85/ (accessed Mar. 27, 2022).spa
dc.relation.references[49] “Agglomerative Hierarchical Clustering,” The Pennsylvania State University. https://online.stat.psu.edu/stat505/lesson/14/14.4 (accessed Mar. 28, 2022).spa
dc.relation.references[50] “Hierarchical Clustering.” https://www.solver.com/xlminer/help/hierarchical-clustering-intro (accessed May 01, 2022).spa
dc.relation.references[51] C. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. Cambridge University Press, 2008.spa
dc.relation.references[52] “Groceries Market Basket Dataset | Kaggle.” https://www.kaggle.com/datasets/irfanasrullah/groceries (accessed Oct. 14, 2022).spa
dc.relation.references[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.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-CompartirIgual 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/spa
dc.subject.ddc000 - Ciencias de la computación, información y obras generalesspa
dc.subject.ddc650 - Gerencia y servicios auxiliares::658 - Gerencia generalspa
dc.subject.lembComportamiento del consumidorspa
dc.subject.lembConsumer behavioreng
dc.subject.proposalMinería de datosspa
dc.subject.proposalAnálisis de cestas de compraspa
dc.subject.proposalReglas de asociaciónspa
dc.subject.proposalDiseño de tiendasspa
dc.subject.proposalData miningeng
dc.subject.proposalMarket basket analysiseng
dc.subject.proposalAssociation ruleseng
dc.subject.proposalStore designeng
dc.titlePropuesta para múltiples diseños de tiendas basados en reglas de asociaciónspa
dc.title.translatedProposal for multiple store layouts based on association ruleseng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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