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.