Categorical classification of tourism dining

Traditional quantitative techniques in foodservice and tourism are unable to discover hidden relationships from a database with numeric and non-numeric data. This paper reports on an initial study about applying an alternative approach that incorporates the rough set theory into relationship modeling in tourism dining. This theory deals with the non-numeric classification analysis of imprecise, uncertain, or incomplete knowledge by incorporating the classical set theory. Using officially published data on tourism dining, decision rules were generated which describe the relationship model. Empirical findings indicated that among the classified cases, 83% of the forecast values were identical to their actual counterparts.