Bøger i Studies in Classification, Data Analysis, and Knowledge Organization serien

This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science. Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis to time series analysis and small area estimation. The applications deal with new developments in a variety of fields, including medicine, finance, engineering, marketing, and cyber risk.The contents comprise selected and peer-reviewed contributions presented at the 13th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2021, held (online) in Florence, Italy, on September 9¿11, 2021. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results at the interface between classification and data science.

This volume presents a selection of peer-reviewed papers that address the latest developments in the methodology and applications of data analysis and classification tools to micro- and macroeconomic problems. The contributions were originally presented at the 30th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2021, held online in Poznan, Poland, September 8-10, 2021.  Providing a balance between methodological and empirical studies, and covering a wide range of topics, the book is divided into five parts focusing on methods and applications in finance, economics, social issues and to COVID-19 data. The book is aimed at a wide audience, including researchers at universities and research institutions, PhD students, as well as practitioners, data scientists and employees in public statistical institutions.

This volume presents the latest advances in statistics and data science, including theoretical, methodological and computational developments and practical applications related to classification and clustering, data gathering, exploratory and multivariate data analysis, statistical modeling, and knowledge discovery and seeking.

This volume gathers peer-reviewed contributions on data analysis, classification and related areas presented at the 28th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2019, held in Szczecin, Poland, on September 18-20, 2019.

This volume presents a selection of research papers on various topics at the interface of statistics and computer science. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics.

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.

This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications.

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks.

This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources.This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.

The topics addressed are from the following fields: Statistics and Data Analysis; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural, and Health Care Sciences; The book presents selected papers from the Second European Conference on Data Analysis, held at Jacobs University Bremen in July 2014.

This volume provides recent research results in data analysis, classification and multivariate statistics and highlights perspectives for new scientific developments within these areas.

This volume contains selected papers covering a wide range of topics, including theoretical and methodological advances relating to data gathering, classification and clustering, exploratory and multivariate data analysis, and knowledge seeking and discovery.

This volume comprises papers dedicated to data science and the extraction of knowledge from many types of data: structural, quantitative, or statistical approaches for the analysis of data;

This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis.

This volume focuses on innovative approaches and recent developments in clustering, analysis of data and models, and applications: The first part of the book covers a broad range of innovations in the area of clustering, from algorithmic innovations for graph clustering to new visualization and evaluation techniques.

This volume presents recent methodological developments in data analysis and classification. It covers a wide range of topics, including methods for classification and clustering, dissimilarity analysis, consensus methods, conceptual analysis of data, and data mining and knowledge discovery in databases.

This book focuses on exploratory data analysis, learning of latent structures in datasets, and unscrambling of knowledge. Coverage details a broad range of methods from multivariate statistics, clustering and classification, visualization and scaling as well as from data and time series analysis.

Large collections of data and information necessitate adequate methods for their analysis. The book presents such methods, proposes and discusses recent approaches and implementations and describes a series of practical applications.

In many fields of science and practice large amounts of dataand informationare collected for analyzing and visualizinglatent structures as orderings or classifications forexample. So, in the first sectionwe find papers on Classification Methods, FuzzyClassification, Multidimensional Scaling, DiscriminantAnalysis and Conceptual Analysis.

Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics.

Proceedings of the 14th Annual Conference of the Gesellschaft fur Klassifikation e.V., University of Marburg March 12-14, 1990

Proceedings of the 19th Annual Conference of the Gesell- schaft fur Klassifikation e.V. University of Basel, March 8-10, 1995.

This volume presents 43 articles dealing with models and methods of data analysis and classification, statistics and stochastics, information systems and WWW- and Internet-related topics as well as many applications.

Proceedings of the 7th Conference on the International Federation of Classification Societies (IFCS-2000), University of Namur, Belgium, 11-14 July, 2000

Presenting contributions to the analysis of data in the information age, this text is aimed at scientists and professionals in the field of classification and statistics. There are research results and applications to economics, archeology, bioinformatics, environment and health.

This is a guide to developments in classification and data analysis, presenting topics which are of central interest to modern statistics. It aims to offer a systematic introduction to the area of classification and data analysis and to transfer advances to a range of applications.

The growing capabilities in generating and collecting data has risen an urgent need of new techniques and tools in order to analyze, classify and summarize statistical information, as well as to discover and characterize trends, and to automatically bag anomalies.

The subject of this work is the incorporation and integration of mathematical and statistical techniques, and information science topics into the field of classification, data analysis, and knowledge organization.