This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017.

This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary.

This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017.

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, Navigating Complexity – Statistical Methods, Data Analysis, and Machine Learning for Actionable Insights, 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.

This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017.

This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary.

This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017.

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, Navigating Complexity – Statistical Methods, Data Analysis, and Machine Learning for Actionable Insights, 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.