Overview
- 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
Part of the book series: Studies in Classification, Data Analysis, and Knowledge Organization (STUDIES CLASS)
Included in the following conference series:
Conference proceedings info: IFCS 2022.
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Table of contents (43 papers)
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Front Matter
Other volumes
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Classification and Data Science in the Digital Age
Keywords
About this book
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 functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education.
The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19–23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification.
Editors and Affiliations
About the editors
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.
Bibliographic Information
Book Title: Classification and Data Science in the Digital Age
Editors: Paula Brito, José G. Dias, Berthold Lausen, Angela Montanari, Rebecca Nugent
Series Title: Studies in Classification, Data Analysis, and Knowledge Organization
DOI: https://doi.org/10.1007/978-3-031-09034-9
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s). 2023
Softcover ISBN: 978-3-031-09033-2Published: 08 December 2023
eBook ISBN: 978-3-031-09034-9Published: 07 December 2023
Series ISSN: 1431-8814
Series E-ISSN: 2198-3321
Edition Number: 1
Number of Pages: XIV, 416
Number of Illustrations: 25 b/w illustrations, 68 illustrations in colour
Topics: Data Structures and Information Theory, Artificial Intelligence, Statistics and Computing/Statistics Programs, Machine Learning, Data Mining and Knowledge Discovery, Statistical Theory and Methods