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In the first article, Masakuna and Kafunda study classifier fusion methods with a focus on cases where prior information is considered. In the second contribution, Kalti and Touil propose a fuzzy k-means approach that is effective for image segmentation in the presence of noise. In the third paper, Roch enriches the growing literature on dissimilarity-based hierarchical clustering. The fourth paper is a fascinating contribution by Zhang, Melnykov, and Melnykov, who develop a mixture of contaminated von Mises-Fisher distributions for clustering directional data with heavy tails.
The fifth paper, by Vahidi, Aghakhani, Martín, Aminzadeh, and Kaveh, introduces a migration-based particle swarm optimization algorithm for the classification of hyperspectral images. In the sixth paper of this issue, Takasawa, Tanioka, and Yadohisa introduce two novel approaches for the important problem of joint analysis with clustering and structural equation modelling. The penultimate paper, by Luo, Li, Yu, and Li, proposes a multiclass sparse discriminant analysis method that incorporates graphical structure among predictors. In the final article of this issue, Tu and Subedi extend recent work by Fang and Subedi (2023)—via the incorporation of a factor analyzer structure—leading to an approach that is more effective for high-dimensional data.
Reference
Fang, Y., & Subedi, S. (2023). Clustering microbiome data using mixtures of logistic normal multinomial models. Scientific Reports, 13(1), 14758.
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McNicholas, P.D. Editorial: Journal of Classification Vol. 40-3. J Classif 40, 467 (2023). https://doi.org/10.1007/s00357-023-09454-y
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DOI: https://doi.org/10.1007/s00357-023-09454-y