Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain. (April 2016)

• Record Type: Journal Article
• Authors: Chen, Li-Fei; Tsai, Chih-Tsung
• Is Part Of: Tourism management. Volume 53(2016)
• Page Start: 197
• Page End: 206
• Publication Date: 2016-04
• Subjects: Location selection -- Data mining -- Rough set theory; Tourism -- Periodicals; 338.4791
• DOI: 10.1016/j.tourman.2015.10.001
• Languages: English
• ISSNs: 0261-5177

Abstract

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. Highlights: A data mining framework was established to support location selection decisions. Rough set theory was applied to predict store performance with location factors. A case of restaurant chain was studied to demonstrate the proposed approach.