Classification and Data Science in the Digital Age / / edited by Paula Brito, José G. Dias, Berthold Lausen, Angela Montanari, Rebecca Nugent.

• Superior document: Studies in Classification, Data Analysis, and Knowledge Organization,
• Author: Brito, Paula.
• TeilnehmendeR: Dias, José G.; Lausen, Berthold.; Montanari, Angela.; Nugent, Rebecca.
• Place / Publishing House: Cham : : Springer International Publishing :, Imprint: Springer,, 2023.
• Year of Publication: 2023
• Edition: 1st ed. 2023.
• Language: English
• Series: Studies in Classification, Data Analysis, and Knowledge Organization,
• Physical Description: 1 online resource (393 pages)

The contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functiona...

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 Government’s Preventing Measures and Health Data Records
• J. Champagne Gareau, É. 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. Dobša 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. García-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. Górecki, M. Šuczak, 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. 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. Dvořák: 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 Analysis of Tweets from UK and US Newspapers.