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  • Conference proceedings
  • © 2022

Classification and Data Science in the Digital Age

  • 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.

Conference proceedings info: IFCS 2022.

About this book

The contributions gathered in this 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.


  • Classification
  • Data Science
  • Clustering
  • Statistical Learning
  • Machine Learning
  • Data Analysis
  • Mutlivariate Analysis
  • Statistical Inference
  • Dimension Reduction
  • Functional Data Analysis
  • Time Series Analysis
  • Network Analysis
  • Open Access

Editors and Affiliations

  • Faculty of Economics, University of Porto, Porto, Portugal

    Paula Brito

  • Business Research Unit, University Institute of Lisbon, Lisbon, Portugal

    José G. Dias

  • Department of Mathematical Sciences, University of Essex, Colchester, United Kingdom

    Berthold Lausen

  • Department of Statistical Sciences "Paolo Fortunati", University of Bologna, Bologna, Italy

    Angela Montanari

  • Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, USA

    Rebecca Nugent

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 in the 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