A fuzzy-based distance to improve empirical methods for menu clustering

An effective menu organization is fundamental to obtain usable applications. A common practice to achieve this is to adopt empirical methods in the menu design phase, by requesting a number of intended final users to provide their ideal tasks arrangements. However, to improve the effectiveness of this approach, it is necessary to filter results, by identifying and discarding data coming from subjects whose mental models are too weak on the considered domain. To this aim, in the paper, we propose a formal tool suited to support menu designers, which is based on a fuzzy-based distance we defined. This measure can be easily calculated on the empirical datasets, thanks to a specifically conceived supporting application we developed. As a result, by exploiting the proposed solution, menu designers can rely on a formal tool to evaluate significance of empirical data, thus leading towards more effective menu clustering.