Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain |
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| Institution: | 1. Department of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan;2. Jie Kune Precision Technologies Co., Ltd., Taoyuan City, Taiwan;1. School of Tourism Management, Sun Yat-sen University 135 Road Xin Gang Xi, Guangzhou, 510275, China;2. School of Tourism Management, South China Normal University, Higher Education Mega Center, Guangzhou, 510006, China;1. Department of Marketing, College of Business, University of Texas at San Antonio, San Antonio, TX 78258, USA;2. Department of Economics, College of Business, University of Texas at San Antonio, San Antonio, TX 78258, USA;1. Department of Tourism, Recreation and Sport Management, University of Florida, USA;2. Department of Tourism, Recreation and Sport Management, University of Florida, FLG 325C, P.O. Box 118208, Florida Gym, Gainesville, 32611, USA;3. Department of Tourism, Recreation and Sport Management,University of Florida, FLG 242C, P.O. Box 118208, Florida Gym, Gainesville, 32611, USA;1. International Centre for Research in Events, Tourism and Hospitality, School of Events, Tourism & Hospitality, Leeds Beckett University, Leeds, UK;2. Research Group in Tourism, Hospitality and Mobilities, School of Tourism and Hospitality Management Sant Ignasi, Ramon Llull University, Barcelona, Spain;3. Departamento de Economía Financiera y Contabilidad I, Facultad de Ciencias Jurídicas y Sociales Universidad Rey Juan Carlos, Madrid, Spain;1. Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina;2. Carolina Population Center, Chapel Hill, North Carolina;3. Department of Economics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina;4. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina |
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| 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. |
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| Keywords: | Location selection Data mining Rough set theory |
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