I would like to begin the second issue of 2021 by thanking the Associate Editors for all of their work over the past year. Their dedication and commitment has eased my transition into the role of Editor-in-Chief. A special note of thanks to Michael Brusco and Fionn Murtagh, who recently stepped down as Associate Editors, for their service to Journal of Classification.

In the first of the ten articles herein, Ng and Murphy develop a model-based clustering approach based on Gaussian Cox processes. Interestingly, a cross-validated likelihood approach is used for model selection. Including a substantial comparison study, the second paper, by Ellenbach, Boulesteix, Bischl, Unger, and Hornung, considers several approaches for tuning parameter values for prediction rules. In the third paper, Torrente and Romo introduce a clever approach for initializing k-means clustering. The fourth paper, by Chacón, introduces explicit agreement extremes for the agreement between two clusterings with given marginals, i.e., for a 2 × 2 table with given marginals. In the fifth paper, McNicholas, McNicholas, and Ashlock develop an evolutionary algorithm, with both crossover and mutation, for parameter estimation in model-based clustering.

The sixth paper of this issue, by Thrun and Ultsch, makes use of projection-based clustering to discover distance-based and density-based clusters in high-dimensional settings. In the seventh paper, Młodak considers using a contiguity constraint for some well-known clustering approaches. The eighth paper, by Fioravanti and Tohmé, considers axioms in group identification problems. In the ninth paper, Randriamihamison, Vialaneix, and Neuvial discuss contiguity constraints for Ward’s hierarchical agglomerative clustering. The tenth and final paper of this issue, by Ogasawara, discusses agreement coefficients for multiple raters as well as their asymptotic results.