In a time when increasingly larger and complex data collections are being produced, it is clear that new and adaptive forms of data representation and analysis have to be conceived and implemented. Distributional data, i.e., data where a distribution rather than a single value is recorded for each descriptor, on each unit, come into this framework. Distributional data may result from the aggregation of large amounts of open/collected/generated data, or it may be directly available in a structured or unstructured form, describing the variability of some features. This book provides models and methods for the representation, analysis, interpretation, and organization of distributional data, taking into account its specific nature, and not relying on a reduction to single values, to be conform to classical paradigms. Conceived as an edited book, gathering contributions from multiple authors, the book presents alternative representations and analysis' methods for distributional data of different types, and in particular, -Uni- and bi-variate descriptive statistics for distributional data -Clustering and classification methodologies -Methods for the representation in low-dimensional spaces -Regression models and forecasting approaches for distribution-valued variables Furthermore, the different chapters -Feature applications to show how the proposed methods work in practice, and how results are to be interpreted, -Often provide information about available software. The methodologies presented in this book constitute cutting-edge developments for stakeholders from all domains who produce and analyse large amounts of complex data, to be analysed in the form of distributions. The book is hence of interest for companies operating not only in the area of data analytics, but also on logistics, energy and finance. It also concerns national statistical institutes and other institutions at European and international level, where microdata is aggregated to preserve confidentiality and allow for analysis at the appropriate regional level. Academics will find in the analysis of distributional data a challenging up-to-date field of research.

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.

This volume presents recent methodological developments in data analysis and classification. A wide range of topics is covered that includes methods for classification and clustering, dissimilarity analysis, graph analysis, consensus methods, conceptual analysis of data, analysis of symbolic data, statistical multivariate methods, data mining and knowledge discovery in databases. Besides structural and theoretical results, the book presents a wide variety of applications, in fields such as biology, micro-array analysis, cyber traffic, bank fraud detection, and text analysis. Combining new methodological advances with a wide variety of real applications, this volume is certainly of special value for researchers and practitioners, providing new analytical tools that are useful in theoretical research and daily practice in classification and data analysis.

The 18th Conference of IASC-ERS, COMPSTAT'2008,is held in Porto,P- tugal,fromAugust24thtoAugust29th2008,locallyorganisedbytheFaculty of Economics of the University of Porto. COMPSTAT is an initiative of the European Regional Section of the Int- national Association for Statistical Computing (IASC-ERS), a section of the International Statistical Institute (ISI). COMPSTAT conferences started in 1974 in Wien; previous editions of COMPSTAT were held in Berlin (2002), Prague (2004) and Rome (2006). It is one of the most prestigious world conferences in Computational Statistics, regularly attracting hundreds of - searchers and practitioners, and has gained a reputation as an ideal forum for presenting top qualitytheoretical and applied work,promoting interdis- plinary researchand establishing contacts amongstresearcherswith common interests. COMPSTAT'2008 is the ?rst edition of COMPSTAT to be hosted by a Portuguese institution. Keynote lectures are addressed by Peter Hall (Department of Mathematics and Statistics, The University of Melbourne), Heikki Mannila (Department of Computer Science, Faculty of Science, University of Helsinki) and Timo Ter. asvirta (School of Economics and Management, University of Aarhus). The conference program includes two tutorials: "Computational Methods in Finance"byJamesGentle(DepartmentofComputationalandDataSciences, George Mason University) and "Writing R Packages" by Friedrich Leisch (Institut fur .. Statistik, Ludwig-Maximilians-Universit. at). Each COMPSTAT meeting is organised with a number of topics highlighted, which lead to - vited Sessions. The Conference program includes also contributed sessions in di?erent topics (both oral communications and posters).