Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain
作者： Li-Fei Chen , Chih-Tsung Tsai
DOI: 10.1016/J.TOURMAN.2015.10.001
关键词:
摘要: Abstract Location selection plays a crucial role in the retail and service industries. A comprehensive location model appropriate analytical technique can improve quality of decisions, attracting more customers substantially impacting market share profitability. This study developed data mining framework based on rough set theory (RST) to support decisions. The proposed consists four stages: (1) problem definition collection; (2) RST analysis; (3) rule validation; (4) knowledge extraction usage. An empirical focused restaurant chain demonstrate validity approach. Twenty variables relevant five aspects were examined, results indicated that latent be identified
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