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Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain


  • Chen, Li-Fei
  • Tsai, Chih-Tsung


Location selection plays a crucial role in the retail and service industries. A comprehensive location selection model and appropriate analytical technique can improve the quality of location decisions, attracting more customers and substantially impacting market share and profitability. This study developed a data mining framework based on rough set theory (RST) to support location selection decisions. The proposed framework consists of four stages: (1) problem definition and data collection; (2) RST analysis; (3) rule validation; and (4) knowledge extraction and usage. An empirical study focused on a restaurant chain to demonstrate the validity of the proposed approach. Twenty location variables relevant to five location aspects were examined, and the results indicated that latent knowledge can be identified to support location selection decisions.

Suggested Citation

  • Chen, Li-Fei & Tsai, Chih-Tsung, 2016. "Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain," Tourism Management, Elsevier, vol. 53(C), pages 197-206.
  • Handle: RePEc:eee:touman:v:53:y:2016:i:c:p:197-206
    DOI: 10.1016/j.tourman.2015.10.001

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    References listed on IDEAS

    1. Pawlak, Zdzislaw, 2002. "Rough sets, decision algorithms and Bayes' theorem," European Journal of Operational Research, Elsevier, vol. 136(1), pages 181-189, January.
    2. Ishizaka, Alessio & Nemery, Philippe & Lidouh, Karim, 2013. "Location selection for the construction of a casino in the Greater London region: A triple multi-criteria approach," Tourism Management, Elsevier, vol. 34(C), pages 211-220.
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    4. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    5. Chen, Li-Fei, 2014. "A novel framework for customer-driven service strategies: A case study of a restaurant chain," Tourism Management, Elsevier, vol. 41(C), pages 119-128.
    6. Su, Chao-Ton & Hsu, Jyh-Hwa, 2006. "Precision parameter in the variable precision rough sets model: an application," Omega, Elsevier, vol. 34(2), pages 149-157, April.
    7. Greco, Salvatore & Matarazzo, Benedetto & Slowinski, Roman, 2001. "Rough sets theory for multicriteria decision analysis," European Journal of Operational Research, Elsevier, vol. 129(1), pages 1-47, February.
    8. Greco, Salvatore & Matarazzo, Benedetto & Slowinski, Roman, 1999. "Rough approximation of a preference relation by dominance relations," European Journal of Operational Research, Elsevier, vol. 117(1), pages 63-83, August.
    9. Pawlak, Zdzislaw, 1997. "Rough set approach to knowledge-based decision support," European Journal of Operational Research, Elsevier, vol. 99(1), pages 48-57, May.
    10. Yu Cao & Guangyu Wan & Fuqiang Wang, 2011. "Predicting Financial Distress Of Chinese Listed Companies Using Rough Set Theory And Support Vector Machine," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 28(01), pages 95-109.
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    Cited by:

    1. Olga Porro & Francesc Pardo-Bosch & Núria Agell & Mónica Sánchez, 2020. "Understanding Location Decisions of Energy Multinational Enterprises within the European Smart Cities’ Context: An Integrated AHP and Extended Fuzzy Linguistic TOPSIS Method," Energies, MDPI, Open Access Journal, vol. 13(10), pages 1-29, May.
    2. Barbati, Maria & Corrente, Salvatore & Greco, Salvatore, 2020. "A general space-time model for combinatorial optimization problems (and not only)," Omega, Elsevier, vol. 96(C).
    3. Derya Celik Turkoglu & Mujde Erol Genevois, 2020. "A comparative survey of service facility location problems," Annals of Operations Research, Springer, vol. 292(1), pages 399-468, September.


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