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

Represents the research results in the field of Classification, Clustering, and Data Analysis. Besides the theoretical analysis, this work features papers that focus on various application fields such as Archaeology, Astronomy, Bio-Sciences, Electronic Data and Web, Finance and Insurance, Library Science and Linguistics, and Music Science.

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

Clustering and Classification, Data Analysis, Data Handling and Business Intelligence are research areas at the intersection of statistics, mathematics, computer science and artificial intelligence.

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.

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.

"Data Analysis" in the broadest sense is the general term for a field of activities of ever-increasing importance in a time called the information age. It covers new areas with such trendy labels as, e.g., data mining or web mining as well as traditional directions emphazising, e.g., classification or knowledge organization.

This volume presents developments in classification and mulitivariate analysis. Topics that have been treated with considerable attention include cluster analysis, discriminant analysis, proximity structure analysis, multidimensional scaling, genetic algorithms, and neural networks.

This volume contains both methodological papers showing new original methods, and papers on applications illustrating how new domain-specific knowledge can be made available from data by clever use of data analysis methods.

This volume contains revised versions of papers selected with respect to the topic "Classification and Information Processing at the Turn of the Millennium". Researchers and practitioners will find the latest results received at the turn of the millennium.

Presents developments in data analysis and classification. This book pays attention to clustering, discrimination, data analysis and statistics, as well as applications in biology, finance and social sciences.

The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems, it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.

Proceedings of the Biannual Meeting of the Classification Group of Societa Italiana di Statistica (SIS), Pescara, July 3-4, 1997

This volume presents a selection of new methods and approaches in the field of Exploratory Data Analysis.

The book presents a range of new developments in the theory and practice of multivariate statistical data analysis. Several contributions illustrate the use of multivariate methods in application fields such as economics, medicine, environment, and biology.

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.

This book presents the most recent methods for analyzing and visualizing symbolic data. It generalizes classical methods of exploratory, statistical and graphical data analysis to the case of complex data. Several benchmark examples from National Statistical Offices illustrate the usefulness of the methods.

This volume describes new methods with special emphasis on classification and cluster analysis. These methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.

The volume presents new developments in data analysis and classification and gives an overview of the state of the art in these scientific fields and relevant applications.

Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation.

A particularly remarkable and unique aspect of his work is that he has been a leading scholar in such diverse areas of research as graph theory and net work models, reliability theory, stochastic optimization, operations research, probability theory, sampling theory, cluster analysis, scaling and multivariate data analysis.

Presents theories, models, algorithms, and applications in clustering, classification, and visualization. This work includes applications of clustering, classification, and visualization in various fields such as marketing, recommendation system, biology, sociology, and social survey.

The conference, which was organized in cooperation with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC), was hosted by Helmut-Schmidt-University, Hamburg, Germany, in July 2008.

The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine.

Data Science and Classification provides new methodological developments in data analysis and classification. The broad and comprehensive coverage includes the measurement of similarity and dissimilarity, methods for classification and clustering, network and graph analyses, analysis of symbolic data, and web mining.

This book presents new developments in data analysis, classification and multivariate statistics, and in their algorithmic implementation. The volume offers contributions to the theory of clustering and discrimination, multidimensional data analysis, data mining, and robust statistics with a special emphasis on the novel Forward Search approach.

This volume provides approaches and solutions to challenges occurring at the interface of research fields such as data analysis, computer science, operations research, and statistics.

The papers in this book cover issues related to the development of novel statistical models for the analysis of data. The book assembles the selected and refereed proceedings of the biannual conference of the Italian Classification and Data Analysis Group (CLADAG), a section of the Italian Statistical Society.