Rosa Maria VAZQUEZ
rvazlean@hotmail.com
Tecnológico Nacional de México, Campus Apizaco, Computer Systems Department, Fco I Madero, Barrio de San José, 90300 Apizaco, Tlaxcala
Edmundo BONILLA
edbonn@hotmail.com
Tecnológico Nacional de México, Campus Apizaco, Computer Systems Department, Fco I Madero, Barrio de San José, 90300 Apizaco, Tlaxcala
Eduardo SANCHEZ
esanlu@hotmail.com
Tecnológico Nacional de México, Campus Apizaco, Computer Systems Department, Fco I Madero, Barrio de San José, 90300 Apizaco, Tlaxcala
Currently, blended food has been a common menu item in fast food restaurants. The sales of the fast-food industry grow thanks to several sales strategies, including the “combos”, so, specialty, regional, family and buffet restaurants are even joining combos’ promotions. This research paper presents the implementation of a system that will serve as support to elaborate combos according to the preferences of the diners using data mining techniques to find relationships between the different dishes that are offered in a restaurant. The software resulting from this research is being used by the mobile application Food Express, with which it communicates through webservices.
References
Agrawal, R. & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. In Proceedings of the 20th International Conference on Very Large Data Bases (pp. 487-499). Santiago de Chile.
Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques. Third Edition. Waltham, USA: Elsevier.
Harikumar, S., & Dilipkumar, D. U. (2016). Apriori algorithm for association rule mining in high dimensional datas. In 2016 International Conference on Data Science and Engineering (ICDSE) (pp. 1–6). Cochin. https://doi.org/10.1109/ICDSE.2016.7823952.
Huang, Y., Lin, Q., & Li, Y. (2018). Apriori-BM Algorithm for Mining Association Rules based on Bit Set Matrix. In 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (pp. 2580–2584). Xi'an. https://doi.org/10.1109/IMCEC.2018.8469367
Kabir, M. M. J., Xu, S., Kang B. H., & Zhao, Z. (2015). Comparative Analysis of Genetic Based Approach and Apriori Algorithm for Mining Maximal Frequent Item Sets. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 39–45). Sendai. https://doi.org/10.1109/CEC.2015.7256872.
Park, S., & Park, Y. (2018). Analysis of Association Between Students' Mathematics Test Results Using Association Rule Mining. In 2018 International Conference on Platform Technology and Service (PlatCon) (pp. 1–6). Jeju. https://doi.org/10.1109/PlatCon.2018.8472756
Pei, S. (2013). Application of data mining technology in the tourism product's marketing CRM. In 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA) (pp.400–403). Toronto, ON. https://doi.org/10.1109/IMSNA.2013.6743300.
Tom, M., & Annaraud, K. (2017). A fuzzy multi-criteria decision making model for menu engineering. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1–6). Naples. https://doi.org/10.1109/FUZZ-IEEE.2017.8015612.
Zheng, X. Z. (2007). Building Personalized Recommendation System in E-Commerce using Association Rule-Based Mining and Classification. In 2007 International Conference on Machine Learning and Cybernetics (pp. 4113–4118). Hong Kong. https://doi.org/10.1109/ICMLC.2007.4370866.
Zulfikar, W. B., Wahana, A., Uriawan, W., & Lukman, N. (2016). Implementation of association rules with apriori algorithm for increasing the quality of promotion. In 2016 4th International Conference on Cyber and IT Service Management (pp. 1–5). Bandung. https://doi.org/10.1109/CITSM.2016.7577586.
