Symbolic Data Analysis Workshop PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Symbolic Data Analysis Workshop PDF full book. Access full book title Symbolic Data Analysis Workshop.

Symbolic Data Analysis and the SODAS Software

Symbolic Data Analysis and the SODAS Software
Author: Edwin Diday
Publisher: John Wiley & Sons
Total Pages: 476
Release: 2008-04-15
Genre: Mathematics
ISBN: 9780470723555

Download Symbolic Data Analysis and the SODAS Software Book in PDF, ePub and Kindle

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.


Analysis of Symbolic Data

Analysis of Symbolic Data
Author: Hans-Hermann Bock
Publisher: Springer Science & Business Media
Total Pages: 444
Release: 2012-12-06
Genre: Mathematics
ISBN: 3642571557

Download Analysis of Symbolic Data Book in PDF, ePub and Kindle

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.


Encyclopedia of Database Technologies and Applications

Encyclopedia of Database Technologies and Applications
Author: Rivero, Laura C.
Publisher: IGI Global
Total Pages: 784
Release: 2005-06-30
Genre: Education
ISBN: 1591407958

Download Encyclopedia of Database Technologies and Applications Book in PDF, ePub and Kindle

"Addresses the evolution of database management, technologies and applications along with the progress and endeavors of new research areas."--P. xiii.


Selected Contributions in Data Analysis and Classification

Selected Contributions in Data Analysis and Classification
Author: Paula Brito
Publisher: Springer Science & Business Media
Total Pages: 619
Release: 2007-08-27
Genre: Computers
ISBN: 3540735585

Download Selected Contributions in Data Analysis and Classification Book in PDF, ePub and Kindle

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 book also presents a wide variety of applications, in fields such as biology, micro-array analysis, cyber traffic, and bank fraud detection.


Data Analysis and Applications 1

Data Analysis and Applications 1
Author: Christos H. Skiadas
Publisher: John Wiley & Sons
Total Pages: 286
Release: 2019-03-04
Genre: Mathematics
ISBN: 1119597579

Download Data Analysis and Applications 1 Book in PDF, ePub and Kindle

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.