Analysis of Symbolic DataAnalysis of Symbolic Data



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.

Author: Hans-Hermann Bock

Publisher: Springer Science & Business Media

ISBN: 9783642571558

Category:

Page: 425

View: 106

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. The book contains an extensive bibliography and a subject index.

Symbolic Data Analysis and the SODAS SoftwareSymbolic Data Analysis and the SODAS Software



This book is the result of the work f a pan-European project team led by Edwin Diday following 3 years work sponsored by EUROSTAT. It includes a full explanation of the new SODAS software developed as a result of this project.

Author: Edwin Diday

Publisher: John Wiley & Sons

ISBN: 0470723556

Category:

Page: 476

View: 334

Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such data. Symbolic data methods differ from that of data mining, for example, because rather than identifying points of interest in the data, symbolic data methods allow the user to build models of the data and make predictions about future events. This book is the result of the work f a pan-European project team led by Edwin Diday following 3 years work sponsored by EUROSTAT. It includes a full explanation of the new SODAS software developed as a result of this project. The software and methods described highlight the crossover between statistics and computer science, with a particular emphasis on data mining.

Symbolic Data AnalysisSymbolic Data Analysis



One consequence of this is that the data may no longer be formatted as single values, but be represented by lists, intervals, distributions, etc.

Author: Lynne Billard

Publisher: John Wiley & Sons Incorporated

ISBN: UOM:39015067671688

Category:

Page: 321

View: 995

With the advent of computers, very large datasets have become routine. Standard statistical methods don’t have the power or flexibility to analyse these efficiently, and extract the required knowledge. An alternative approach is to summarize a large dataset in such a way that the resulting summary dataset is of a manageable size and yet retains as much of the knowledge in the original dataset as possible. One consequence of this is that the data may no longer be formatted as single values, but be represented by lists, intervals, distributions, etc. The summarized data have their own internal structure, which must be taken into account in any analysis. This text presents a unified account of symbolic data, how they arise, and how they are structured. The reader is introduced to symbolic analytic methods described in the consistent statistical framework required to carry out such a summary and subsequent analysis. Presents a detailed overview of the methods and applications of symbolic data analysis. Includes numerous real examples, taken from a variety of application areas, ranging from health and social sciences, to economics and computing. Features exercises at the end of each chapter, enabling the reader to develop their understanding of the theory. Provides a supplementary website featuring links to download the SODAS software developed exclusively for symbolic data analysis, data sets, and further material. Primarily aimed at statisticians and data analysts, Symbolic Data Analysis is also ideal for scientists working on problems involving large volumes of data from a range of disciplines, including computer science, health and the social sciences. There is also much of use to graduate students of statistical data analysis courses.

Ordinal and Symbolic Data AnalysisOrdinal and Symbolic Data Analysis



The new methods, developed here, are able to treat more complex or ordered data and express more knowledge than conventional data tables.

Author: Edwin Diday

Publisher: Springer

ISBN: 3642611605

Category:

Page: 372

View: 928

Ordinal and symbolic data are increasingly finding applications in a number of domains, e.g. information technologies, economics, medicine. Here, one of the best teams working in the field presents the most recent advances in ordinal and symbolic data analysis. The new methods, developed here, are able to treat more complex or ordered data and express more knowledge than conventional data tables.

Ordinal and Symbolic Data AnalysisOrdinal and Symbolic Data Analysis



The new methods, developed here, are able to treat more complex or ordered data and express more knowledge than conventional data tables.

Author: Edwin Diday

Publisher: Springer

ISBN: 3540610812

Category:

Page: 372

View: 881

Ordinal and symbolic data are increasingly finding applications in a number of domains, e.g. information technologies, economics, medicine. Here, one of the best teams working in the field presents the most recent advances in ordinal and symbolic data analysis. The new methods, developed here, are able to treat more complex or ordered data and express more knowledge than conventional data tables.

Symbolic Data AnalysisSymbolic Data Analysis



of interest, aggregation over the dataset produces symbolic data such as that in Table 2.20. Note that instead of State, the concept could be City. Table 2.20 Lung cancer treatments – by State State Y30 = # Lung Cancer Treatments ...

Author: Lynne Billard

Publisher: John Wiley & Sons

ISBN: 9780470090176

Category:

Page: 330

View: 612

With the advent of computers, very large datasets have become routine. Standard statistical methods don’t have the power or flexibility to analyse these efficiently, and extract the required knowledge. An alternative approach is to summarize a large dataset in such a way that the resulting summary dataset is of a manageable size and yet retains as much of the knowledge in the original dataset as possible. One consequence of this is that the data may no longer be formatted as single values, but be represented by lists, intervals, distributions, etc. The summarized data have their own internal structure, which must be taken into account in any analysis. This text presents a unified account of symbolic data, how they arise, and how they are structured. The reader is introduced to symbolic analytic methods described in the consistent statistical framework required to carry out such a summary and subsequent analysis. Presents a detailed overview of the methods and applications of symbolic data analysis. Includes numerous real examples, taken from a variety of application areas, ranging from health and social sciences, to economics and computing. Features exercises at the end of each chapter, enabling the reader to develop their understanding of the theory. Provides a supplementary website featuring links to download the SODAS software developed exclusively for symbolic data analysis, data sets, and further material. Primarily aimed at statisticians and data analysts, Symbolic Data Analysis is also ideal for scientists working on problems involving large volumes of data from a range of disciplines, including computer science, health and the social sciences. There is also much of use to graduate students of statistical data analysis courses.

Selected Contributions in Data Analysis and ClassificationSelected Contributions in Data Analysis and Classification



This volume presents recent methodological developments in data analysis and classification.

Author: Paula Brito

Publisher: Springer Science & Business Media

ISBN: 9783540735588

Category:

Page: 634

View: 882

"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, 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."--Publisher's website.

Advances in Data ScienceAdvances in Data Science



These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap.

Author: Edwin Diday

Publisher: John Wiley & Sons

ISBN: 9781786305763

Category:

Page: 258

View: 243

Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.

Clustering Methodology for Symbolic DataClustering Methodology for Symbolic Data



Covers everything readers need to know about clustering methodology for symbolic data—including new methods and headings—while providing a focus on multi-valued list data, interval data and histogram data This book presents all of the ...

Author: Lynne Billard

Publisher: John Wiley & Sons

ISBN: 9781119010388

Category:

Page: 352

View: 643

Covers everything readers need to know about clustering methodology for symbolic data—including new methods and headings—while providing a focus on multi-valued list data, interval data and histogram data This book presents all of the latest developments in the field of clustering methodology for symbolic data—paying special attention to the classification methodology for multi-valued list, interval-valued and histogram-valued data methodology, along with numerous worked examples. The book also offers an expansive discussion of data management techniques showing how to manage the large complex dataset into more manageable datasets ready for analyses. Filled with examples, tables, figures, and case studies, Clustering Methodology for Symbolic Data begins by offering chapters on data management, distance measures, general clustering techniques, partitioning, divisive clustering, and agglomerative and pyramid clustering. Provides new classification methodologies for histogram valued data reaching across many fields in data science Demonstrates how to manage a large complex dataset into manageable datasets ready for analysis Features very large contemporary datasets such as multi-valued list data, interval-valued data, and histogram-valued data Considers classification models by dynamical clustering Features a supporting website hosting relevant data sets Clustering Methodology for Symbolic Data will appeal to practitioners of symbolic data analysis, such as statisticians and economists within the public sectors. It will also be of interest to postgraduate students of, and researchers within, web mining, text mining and bioengineering.

New Approaches in Classification and Data AnalysisNew Approaches in Classification and Data Analysis



The subject of this book is the analysis and processing of structural or quantitative data with emphasis on classification methods, new algorithms as well as applications in various fields related to data analysis and classification.

Author: Edwin Diday

Publisher: Springer Science & Business Media

ISBN: 9783642511752

Category:

Page: 693

View: 837

The subject of this book is the analysis and processing of structural or quantitative data with emphasis on classification methods, new algorithms as well as applications in various fields related to data analysis and classification. The book presents the state of the art in world-wide research and application of methods from the fields indicated above and consists of survey papers as well as research papers.

Object Oriented Data AnalysisObject Oriented Data Analysis



That paper also considered unit sphere versus simplex data object representations and found the best performance in that case came from working directly on the unit simplex. 18.4 Symbolic Data Analysis Another statistical ...

Author: J. S. Marron

Publisher: CRC Press

ISBN: 9781351189668

Category:

Page: 436

View: 392

Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research through new terminology for discussing the often many possible approaches to the analysis of complex data. Such data are naturally arising in a wide variety of areas. This book aims to provide ways of thinking that enable the making of sensible choices. The main points are illustrated with many real data examples, based on the authors' personal experiences, which have motivated the invention of a wide array of analytic methods. While the mathematics go far beyond the usual in statistics (including differential geometry and even topology), the book is aimed at accessibility by graduate students. There is deliberate focus on ideas over mathematical formulas. J. S. Marron is the Amos Hawley Distinguished Professor of Statistics, Professor of Biostatistics, Adjunct Professor of Computer Science, Faculty Member of the Bioinformatics and Computational Biology Curriculum and Research Member of the Lineberger Cancer Center and the Computational Medicine Program, at the University of North Carolina, Chapel Hill. Ian L. Dryden is a Professor in the Department of Mathematics and Statistics at Florida International University in Miami, has served as Head of School of Mathematical Sciences at the University of Nottingham, and is joint author of the acclaimed book Statistical Shape Analysis.

Symbolic Data AnalysisSymbolic Data Analysis



Interval-valued data are one of the most common forms of symbolic data.

Author: Yaotong Cai

Publisher:

ISBN: OCLC:1060584681

Category:

Page: 408

View: 879

Interval-valued data are one of the most common forms of symbolic data. Previous studies have provided a number of approaches to conduct linear regression models for interval data, while few have involved issues surrounding inference on the regression coefficient estimates. In this dissertation, we propose a method of statistical inference on coefficient estimates for interval data regression by means of the maximum likelihood principle. Under some assumptions, this method not only enables us to provide point estimators of the parameters in linear regression models, but also gives the distributions of the point estimators, as well as the confidence intervals. Performances of the proposed method are evaluated by simulations as well as real data analyses.

Advances in Multivariate Data AnalysisAdvances in Multivariate Data Analysis



Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, University of Palermo, July 5–6, 2001 Hans-Hermann Bock, Marcello Chiodi, Antonio Mineo. A Modal Symbolic Pattern ...

Author: Hans-Hermann Bock

Publisher: Springer Science & Business Media

ISBN: 9783642171116

Category:

Page: 281

View: 521

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.

Data Analysis Classification and Related MethodsData Analysis Classification and Related Methods



This volume contains a selection of papers presented at the Seven~h Confer ence of the International Federation of Classification Societies (IFCS-2000), which was held in Namur, Belgium, July 11-14,2000.

Author: Henk A.L. Kiers

Publisher: Springer Science & Business Media

ISBN: 9783642597893

Category:

Page: 428

View: 194

This volume contains a selection of papers presented at the Seven~h Confer ence of the International Federation of Classification Societies (IFCS-2000), which was held in Namur, Belgium, July 11-14,2000. From the originally sub mitted papers, a careful review process involving two reviewers per paper, led to the selection of 65 papers that were considered suitable for publication in this book. The present book contains original research contributions, innovative ap plications and overview papers in various fields within data analysis, classifi cation, and related methods. Given the fast publication process, the research results are still up-to-date and coincide with their actual presentation at the IFCS-2000 conference. The topics captured are: • Cluster analysis • Comparison of clusterings • Fuzzy clustering • Discriminant analysis • Mixture models • Analysis of relationships data • Symbolic data analysis • Regression trees • Data mining and neural networks • Pattern recognition • Multivariate data analysis • Robust data analysis • Data science and sampling The IFCS (International Federation of Classification Societies) The IFCS promotes the dissemination of technical and scientific information data analysis, classification, related methods, and their applica concerning tions.

Data Analysis and Applications 1Data Analysis and Applications 1



This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications.

Author: Christos H. Skiadas

Publisher: John Wiley & Sons

ISBN: 9781119597575

Category:

Page: 286

View: 510

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications. Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural equations, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining.