Navigating Complexity

Statistical Methods, Data Analysis, and Machine Learning for Actionable Insights
Erscheinungsjahr 2026
ISBN: 978-3-032-32008-7
Verlag: Springer

Buch, Englisch, 304 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Studies in Classification, Data Analysis, and Knowledge Organization

This volume gathers selected, peer-reviewed contributions presented at the 19th Conference of the International Federation of Classification Societies (IFCS 2026), held on 14–16 July 2026 in Milan, Italy. Reflecting the volume’s motto, , the papers showcase modern methodologies and real-world applications designed to extract actionable insights from intricate data. The topics span a wide range of areas within statistics, machine learning, and data science, including model-based clustering, Bayesian methods, anomaly detection, and predictive modeling. Novel and tailored models are proposed for complex data types, such as mixed-type, compositional, and functional data. Furthermore, advanced statistical concepts and cutting-edge artificial intelligence techniques are explored, underscoring the interdisciplinary effort required to address complex challenges in today’s data-driven landscape.

Founded in 1985, the International Federation of Classification Societies (IFCS) and its biennial conference are dedicated to promoting mutual communication, cooperation, and the interchange of views among those interested in the scientific principles, numerical methods, and practice of data science, data analysis, and classification.

Zielgruppe

Research

Autoren/Hrsg.

Brito, Paula

Denti, Francesco

Greselin, Francesca

Jajuga, Krzysztof

Zenga, Mariangela

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software

Über Autor(innen)

Paula Brito is a Professor at the Faculty of Economics of the University of Porto and Researcher at INESC TEC, Portugal. She holds a PhD in applied mathematics from Paris Dauphine University and a habilitation in applied mathematics from the University of Porto. Her research focuses on multidimensional complex data, known as symbolic data, for which she develops statistical models and multivariate analysis methodologies.

Francesco Denti is an Assistant Professor at the Department of Statistics, University of Padua. Italy. He previously held positions at Università Cattolica del Sacro Cuore in Milan and the University of California, Irvine. He is interested in Bayesian mixtures, Bayesian nonparametrics, model-based clustering, shrinkage priors, and dimensionality reduction.

Francesca Greselin is Professor of Statistics at the University of Milano-Bicocca. Her research focuses on mixture models for classification and clustering, with particular emphasis on robust methods for data analysis. Her work spans computational statistics, and the development of new inferential methods for the study of economic inequality. She is Past President of CLADAG-SIS and serves as Associate Editor of Statistics and Computing.

Krzysztof Jajuga is a Professor of Finance at Wroclaw University of Economics and Business, Poland. He holds a doctoral and habilitation degree from Wroclaw University of Economics and Business and the titular professor given by the President of Poland. He is the President of the International Federation of Classification Societies (IFCS). He has authored papers and monographs in financial markets, risk management, data analysis, artificial intelligence, multivariate statistics, econometrics and real estate in Polish and English.

Mariangela Zenga is an Associate Professor in Social Statistics at the University of Milano-Bicocca, Italy. Her research interests are in models to study the flows of patients in hospitals, in the gender gap in higher education and in labour market studies.