Symbolic data analysis and the SODAS software [[electronic resource] /] / edited by Edwin Diday, Monique Noirhomme-Fraiture

• Pubblicazione: Chichester, England ; ; Hoboken, NJ, : J. Wiley & Sons, c2008
• Descrizione fisica: 1 online resource (477 p.)
• Disciplina: 005.74; 519.535
• Soggetto topico: Data mining
• Altri autori: DidayE Noirhomme-FraitureMonique
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Symbolic Data Analysis and the SODAS Software; Contents; Contributors; Foreword; Preface; ASSO Partners; Introduction; 1 The state of the art in symbolic data analysis: overview and future; Part I Databases versus Symbolic Objects; 2 Improved generation of symbolic objects from relational databases; 3 Exporting symbolic objects to databases; 4 A statistical metadata model for symbolic objects; 5 Editing symbolic data; 6 The normal symbolic form; 7 Visualization; Part II Unsupervised Methods; 8 Dissimilarity and matching; 9 Unsupervised divisive classification ; 10 Hierarchical and pyramidal clustering11 Clustering methods in symbolic data analysis; 12 Visualizing symbolic data by Kohonen maps; 13 Validation of clustering structure: determination of the number of clusters; 14 Stability measures for assessing a partition and its clusters: application to symbolic data sets; 15 Principal component analysis of symbolic data described by intervals; 16 Generalized canonical analysis; Part III Supervised Methods; 17 Bayesian decision trees; 18 Factor discriminant analysis; 19 Symbolic linear regression methodology ; 20 Multi-layer perceptrons and symbolic dataPart IV Applications and the SODAS Software; 21 Application to the Finnish, Spanish and Portuguese data of the European Social Survey; 22 People's life values and trust components in Europe: symbolic data analysis for 20-22 countries; 23 Symbolic analysis of the Time Use Survey in the Basque country; 24 SODAS2 software: Overview and methodology; Index
• Sommario/riassunto: 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
• Titolo autorizzato: Symbolic data analysis and the SODAS software
• ISBN: 1-281-30833-1; 9786611308339; 0-470-72356-4; 0-470-72355-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910831069403321
• Lo trovi qui: Univ. Federico II