Data-driven market segmentation is a popular and widely used segmentation method in tourism. It aims to identify market segments among tourists who are similar to each other, thus allowing a targeted marketing mix to be developed. Typically data used to segment tourists are characterized by small numbers of respondents and large numbers of survey questions. Small samples and numerous questions cause serious methodological problems that have typically been addressed by using factorcluster analysis to reduce the dimensionality of data. Recently, factor-cluster analysis has been shown as an unacceptable solution to the problem of high data dimensionality in segmentation. In this article, the authors introduce biclustering, a novel approach to address the problem of high dimensionality in tourism segmentation studies. We discuss the circumstances in which biclustering should be used rather than parametric or nonparametric grouping techniques. An illustrative example of how biclustering is computed is also provided.