Classification and Data Science in the Digital Age

Hayashi, E. Hoshino, M. Suzuki, E. Nakanishi, K. Sakai, and M. Obatake: Detection of the Biliary Atresia Using Deep Convolutional Neural Networks Based on Statistical Learning Weights via Optimal Similarity and Resampling Methods.- Ch. Hennig: Some Issues in Robust Clustering.- J. Kalina and P. Janá£ek: Robustness Aspects of Optimized Centroids.- L. Labiod and M. Nadif: Data Clustering and Representation Learning Based on Networked Data.- Lazhar Labiod and Mohamed Nadif: Towards a Bi-stochastic Matrix Approximation of k-means and Some Variants.- A. LaLonde, T. Love, D. R. Young, and T. Wu: Clustering Adolescent Female Physical Activity Levels with an Infinite Mixture Model on Random Effects.- Á. López-Oriona, J. A. Vilar, and P. D'Urso: Unsupervised Classification of Categorical Time Series Through Innovative Distances.- D. Masís, E. Segura, J. Trejos, and A. Xavier: Fuzzy Clustering by Hyperbolic Smoothing.- R. Meng, H. K. H. Lee, and K. Bouchard: Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks.- H. Duy Nguyen, F. Forbes, G. Fort, and O. Cappé: An Online Minorization-Maximization Algorithm.- L. Palazzo and R. Ievoli: Detecting Differences in Italian Regional Health Services During Two Covid-19 Waves.- G. Panagiotidou and T. Chadjipadelis: Political and Religion Attitudes in Greece: Behavioral Discourses.- K. Pawlasová, I. Karafiátová, and J. Dvorák: Supervised Classification via Neural Networks for Replicated Point Patterns.- G. Perrone and G. Soffritti: Parsimonious Mixtures of Seemingly Unrelated Contaminated Normal Regression Models.- N. Pronello, R. Ignaccolo, L. Ippoliti, and S. Fontanella: Penalized Model-based Functional Clustering: a Regularization Approach via Shrinkage Methods.- D. Rodrigues, L. P. Reis, and B. M. Faria: Emotion Classification Based on Single Electrode Brain Data: Applications for Assistive Technology.- R. Scimone, A. Menafoglio, L. M. Sangalli, and P. Secchi: The Death Process in Italy Before and During the Covid-19 Pandemic: a Functional Compositional Approach.- O. Silva, Á. Sousa, and H. Bacelar-Nicolau: Clustering Validation in the Context of Hierarchical Cluster Analysis: an Empirical Study.- C. Silvestre, M. G. M. S. Cardoso, and M. Figueiredo: An MML Embedded Approach for Estimating the Number of Clusters.- Á. Sousa, O. Silva, M. Graça Batista, S. Cabral, and H. Bacelar-Nicolau: Typology of Motivation Factors for Employees in the Banking Sector: An Empirical Study Using Multivariate Data Analysis Methods.- J. Michael Spoor, J. Weber, and J. Ovtcharova: A Proposal for Formalization and Definition of Anomalies in Dynamical Systems.- N. Tahiri and A. Koshkarov: New Metrics for Classifying Phylogenetic Trees Using -means and the Symmetric Difference Metric.- S. D. Tomarchio: On Parsimonious Modelling via Matrix-variate t Mixtures.- G. Zammarchi, M. Romano, and C. Conversano: Evolution of Media Coverage on Climate Change and Environmental Awareness: an Analysisof Tweets from UK and US Newspapers.

field of supervised and unsupervised classification, dimension reduction, data science and machine learning.
Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science and Head of the Statistics & Data Science Department, Pittsburgh, PA, USA. She won the ASA Waller Award for Innovation in Statistics Education and serves as co-editor of the book series Springer Texts in Statistics. She focuses on clustering and classification methodology with emphasis on large high-dimensional record linkage applications.