Fuzzy Double Clustering: A Robust Proposal

In this paper a robust fuzzy methodology for simultaneously clustering objects and variables is proposed. Starting from Double kMeans, different fuzzy generalizations for categorical multivariate data have been proposed in literature which are not appropriate for heterogeneous two-mode datasets, especially if outliers occur. In practice, in these cases, the existing fuzzy procedures do not recognize them. In order to overcome that inconvenience and to take into account a certain amount of outlying observations a new fuzzy approach with noise clusters for the objects and variables is introduced and discussed.