The 41st volume of the Journal of Classification begins with an article, by Karlsson and Hössjer, that marks an important contribution to Bayesian set-valued classification, and the second contribution is an erratum thereto. In the third paper, Hou-Liu and Browne contribute to the rapidly growing literature on model-based clustering by considering nested Gaussian clusters via orthogonal intrinsic variable subspaces. The fourth paper is an interesting contribution by McLaughlin, Franczak, and Kashlak, who developed a parsimonious family of contaminated shifted asymmetric Laplace distributions for clustering. The fifth paper, by Lim, introduces a new non-parametric polytomous attributes diagnostic classification method that can be used with a variety of cognitive diagnosis models.

In the sixth paper of this issue, Chen, Peng, Nie, and Kong contribute to the literature on the search for low-dimensional clustering subspaces. The seventh paper, by Gaye, Ka Diongue, Sylla, Diarra, Diallo, Talla, and Loucoubar, proposes an approach for supervised classification of high-dimensional, correlated, data. This approach is then illustrated via a genomic dataset comprising over 700,000 single nucleotide polymorphisms for 445 individuals. In the penultimate paper, Nam, Mun, Jo, and Kim combine a weighted support vector machine and Gaussian mixture-based oversampling to develop an approach for the supervised classification of infrequent events. In the final article of this issue, de Rooij considers restricted multidimensional unfolding within a multinomial logistic framework for supervised classification.