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

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  • Chen, Li-Fei
  • Tsai, Chih-Tsung

Abstract

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

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    Cited by:

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    2. 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, vol. 13(10), pages 1-29, May.
    3. Tooraj Karimi & Arvin Hojati & Jeffrey Yi-Lin Forrest, 2022. "A new methodology for sustainability measurement of banks based on rough set theory," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 415-431, March.
    4. Han, Shuihua & Jia, Xinyun & Chen, Xinming & Gupta, Shivam & Kumar, Ajay & Lin, Zhibin, 2022. "Search well and be wise: A machine learning approach to search for a profitable location," Journal of Business Research, Elsevier, vol. 144(C), pages 416-427.
    5. Chen, Linlin & Han, Shuihua & Ye, Zhen & Xia, Senmao, 2023. "The optimisation of the location of front distribution centre: A spatio-temporal joint perspective," International Journal of Production Economics, Elsevier, vol. 263(C).
    6. Barbati, Maria & Corrente, Salvatore & Greco, Salvatore, 2020. "A general space-time model for combinatorial optimization problems (and not only)," Omega, Elsevier, vol. 96(C).
    7. 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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