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Abstract: Cluster analysis is commonly used for classifying subjects, but the analytical technique often receives skepticisms of the way of measuring of similarity and the number of clusters. Despite applying discriminant analysis can improve target segmentation accuracy; this analytical technique is less adopted. Hence, the underlying purpose of this paper is to demonstrate how cluster analysis in conjunction with discriminant analysis can be applied in a multifaceted business field in tourism research as targeting an optimal market segments is crucial to organization success. With the growth of cross border shopping between the Pearl River Delta Regions in Greater China after the launch of "one year multiple endorsements" of the Individual Visit Scheme by the Chinese government, many popular international brands start to set up shops in Hong Kong and Macau to capture the emerging market. As such, this study adopted hierarchical cluster analysis, followed by K means cluster analysis to classify cross border shoppers into mutually exclusive groups based on their motivation and attitudes in the context. Cross tabulation analysis was then conducted to test if there is any association between the product/service purchased and the cluster membership of respondents. Finally, discriminant analysis was employed to assess the adequacy of classification, and to determine which variables are the best predictors of group membership so that the variables can be used to predict new cases of group membership in the context. To achieve the research purpose, quantitative research design was adopted and data was collected using intercept method with convenience sampling technique. A total of 194 respondents who normally reside in Mainland China were recruited, and their motivation and attitudes on cross-border shopping in Hong Kong were measured. Results of the cluster analysis suggest that there exist three distinct groups of cross-border shoppers based on the motivational and attitudinal criteria. The attitudes towards product price and quality, agglomeration of comprehensive retails, together with age, marital status, education, occupation, types of goods purchased, and frequency of visit are significantly different among the three groups. The cross tabulation analysis reveals that there was an association between cluster membership of respondents and purchase of high involvement products such as photographic equipment, and certain food products. Using discriminant analysis, the group membership of tourists was predicted based on...