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BuchKartoniert, Paperback
416 Seiten
Englisch
Springererschienen am08.12.20231st ed. 2023
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.mehr

Produkt

KlappentextThe contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications.
Zusammenfassung
Presents the latest advances in data science, classification, and machine learning

Covers methodological approaches to data science problems and real-world applications in different areas

Benefits researchers and practitioners in data science, machine learning, classification, and related fields

This book is open access, which means that you have free and unlimited access

This book is open access, which means that you have free and unlimited access.
Details
ISBN/GTIN978-3-031-09033-2
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2023
Erscheinungsdatum08.12.2023
Auflage1st ed. 2023
ReiheStudies in Classification, Data Analysis, and Knowledge Organization
Seiten416 Seiten
SpracheEnglisch
IllustrationenXIV, 416 p. 93 illus., 68 illus. in color.
Artikel-Nr.16553046

Inhalt/Kritik

Inhaltsverzeichnis
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. Dobsa 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. Suczak, 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. DvoÅák: 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.mehr

Schlagworte

Autor

Paula Brito is a Professor at the Faculty of Economics at the University of Porto, and a member of the Artificial Intelligence and Decision Support Research Group (LIAAD) of INESC TEC, Portugal. She holds a PhD in Applied Mathematics from the University Paris Dauphine, and a Habilitation in Applied Mathematics from the University of Porto. Her research focuses on the analysis of multidimensional complex data, known as symbolic data, for which she develops statistical approaches and multivariate analysis methodologies.

José G. Dias is an Associate Professor with habilitation at the University Institute of Lisbon (Iscte), Portugal. He holds a PhD in Economics from the University of Groningen. At the Iscte, he is currently Deputy Director of the Department of Quantitative Methods and the coordinator of the research group in Data Analytics. He was President of the Portuguese Association for Classification and Data Analysis (CLAD). He has been an active researcher inthe field of clustering.
Berthold Lausen is a Full Professor of Data Science at the Department of Mathematical Sciences at the University of Essex, Colchester, UK, former president of the International Federation of Classification Societies (IFCS) from 2018 to 2019, former president of the Data Science Society (GfKl) from 2013 to 2019 and founding vice president of the European Association for Data Science (EuADS) from 2015 to 2018. His research interests are in the field of artificial intelligence, biostatistics, classification, clinical research, data science and machine learning.
Angela Montanari is a Full Professor of Statistics and past Head of the Department of Statistical Sciences of the University of Bologna, Italy, the president of the International Federation of Classification Societies (IFCS) from 2020 to 2021, and former president of the Classification Group of the Italian Statistical Society (CLADAG) from 2007 to 2009.Her research interests are in the 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.
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