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Table of Contents
Preface
R. Abdesselam: A Topological Clustering of Individuals
C. Anton and I. Smith: Model Based Clustering of Functional Data with Mild Outliers
F. Antonazzo and S. Ingrassia: A Trivariate Geometric Classification of Decision Boundaries for Mixtures of Regressions
E. Arnone, E. Cunial, and L. M. Sangalli: Generalized Spatio-temporal Regression with PDE Penalization
R. Ascari and S. Migliorati: A New Regression Model for the Analysis of Microbiome Data
R. Aschenbruck, G. Szepannek, and A. F. X. Wilhelm: Stability of Mixed-type Cluster Partitions for Determination of the Number of Clusters
A. Ashofteh and P. Campos: A Review on Official Survey Item Classification for Mixed-Mode Effects Adjustment
V. Batagelj: Clustering and Blockmodeling Temporal Networks Two Indirect Approaches
R. Boutalbi, L. Labiod, and M. Nadif: Latent Block Regression Model
N. Chabane, M. Achraf Bouaoune, R. Amir Sofiane Tighilt, B. Mazoure, N. Tahiri, and V. Makarenkov: Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations
T. Chadjipadelis and S. Magopoulou: COVID-19 Pandemic: a Methodological Model for the Analysis of Governments Preventing Measures and Health Data Records
J. Champagne Gareau, . Beaudry, and V. Makarenkov: pcTVI: Parallel MDP Solver Using a Decomposition into Independent Chains
C. Di Nuzzo and S. Ingrassia: Three-way Spectral Clustering
J. Doba and H. A. L. Kiers: Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space
J. Gama: Trends in Data Stream Mining
L. A. Garca-Escudero, A. Mayo-Iscar, G. Morelli, and M. Riani: Old and New Constraints in Model Based Clustering
V. G Genova, G. Giordano, G . Ragozini, and M. Prosperina Vitale: Clustering Student Mobility Data in 3-way Networks
R. Giubilei: Clustering Brain Connectomes Through a Density-peak Approach
T. Grecki, M. uczak, and P. Piasecki: Similarity Forest for Time Series Classification
K. 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. Janek: 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
. Lpez-Oriona, J. A. Vilar, and P. DUrso: Unsupervised Classification of Categorical Time Series Through Innovative Distances
D. Mass, 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. Capp: 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. Pawlasov, I. Karafitov, and J. Dvok: 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, . 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
. Sousa, O. Silva, M. Graa 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 Analysis of Tweets from UK and US Newspapers.
R. Abdesselam: A Topological Clustering of Individuals
C. Anton and I. Smith: Model Based Clustering of Functional Data with Mild Outliers
F. Antonazzo and S. Ingrassia: A Trivariate Geometric Classification of Decision Boundaries for Mixtures of Regressions
E. Arnone, E. Cunial, and L. M. Sangalli: Generalized Spatio-temporal Regression with PDE Penalization
R. Ascari and S. Migliorati: A New Regression Model for the Analysis of Microbiome Data
R. Aschenbruck, G. Szepannek, and A. F. X. Wilhelm: Stability of Mixed-type Cluster Partitions for Determination of the Number of Clusters
A. Ashofteh and P. Campos: A Review on Official Survey Item Classification for Mixed-Mode Effects Adjustment
V. Batagelj: Clustering and Blockmodeling Temporal Networks Two Indirect Approaches
R. Boutalbi, L. Labiod, and M. Nadif: Latent Block Regression Model
N. Chabane, M. Achraf Bouaoune, R. Amir Sofiane Tighilt, B. Mazoure, N. Tahiri, and V. Makarenkov: Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations
T. Chadjipadelis and S. Magopoulou: COVID-19 Pandemic: a Methodological Model for the Analysis of Governments Preventing Measures and Health Data Records
J. Champagne Gareau, . Beaudry, and V. Makarenkov: pcTVI: Parallel MDP Solver Using a Decomposition into Independent Chains
C. Di Nuzzo and S. Ingrassia: Three-way Spectral Clustering
J. Doba and H. A. L. Kiers: Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space
J. Gama: Trends in Data Stream Mining
L. A. Garca-Escudero, A. Mayo-Iscar, G. Morelli, and M. Riani: Old and New Constraints in Model Based Clustering
V. G Genova, G. Giordano, G . Ragozini, and M. Prosperina Vitale: Clustering Student Mobility Data in 3-way Networks
R. Giubilei: Clustering Brain Connectomes Through a Density-peak Approach
T. Grecki, M. uczak, and P. Piasecki: Similarity Forest for Time Series Classification
K. 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. Janek: 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
. Lpez-Oriona, J. A. Vilar, and P. DUrso: Unsupervised Classification of Categorical Time Series Through Innovative Distances
D. Mass, 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. Capp: 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. Pawlasov, I. Karafitov, and J. Dvok: 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, . 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
. Sousa, O. Silva, M. Graa 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 Analysis of Tweets from UK and US Newspapers.