Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex DataHans-Hermann Bock, Edwin Diday Springer Science & Business Media, 6 déc. 2012 - 425 pages Raymond Bisdorff CRP-GL, Luxembourg The development of the SODAS software based on symbolic data analysis was extensively described in the previous chapters of this book. It was accompanied by a series of benchmark activities involving some official statistical institutes throughout Europe. Partners in these benchmark activities were the National Statistical Institute (INE) of Portugal, the Instituto Vasco de Estadistica Euskal (EUSTAT) from Spain, the Office For National Statistics (ONS) from the United Kingdom, the Inspection Generale de la Securite Sociale (IGSS) from Luxembourg 1 and marginally the University of Athens . The principal goal of these benchmark activities was to demonstrate the usefulness of symbolic data analysis for practical statistical exploitation and analysis of official statistical data. This chapter aims to report briefly on these activities by presenting some signifi cant insights into practical results obtained by the benchmark partners in using the SODAS software package as described in chapter 14 below. |
Table des matières
1 | |
8 | |
Symbolic Approaches for Threeway Data | 12 |
The Classical Data Situation | 24 |
Symbolic Data | 39 |
Symbolic Objects | 54 |
Generation of Symbolic Objects from Relational Databases | 78 |
Descriptive Statistics for Symbolic Data | 106 |
Symbolic Factor Analysis | 198 |
Assigning Symbolic Objects to Classes | 234 |
Clustering Methods for Symbolic Objects | 294 |
Illustrative Benchmark Analyses | 352 |
25 | 353 |
The SODAS Software Package | 386 |
Notations and Abbreviations | 392 |
Addresses of Contributors to this Volume | 414 |
Visualizing and Editing Symbolic Objects | 125 |
F Esposito D Malerba V Tamma H H Bock | 139 |
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Expressions et termes fréquents
A₁ algorithm assertion object B₁ Boolean symbolic objects Cartesian product classes classes C1 classical data clustering coding coefficient colour column compute consider corresponding criterion d₁ data matrix data vector database decision tree decisional node defined definition denoted density described description set descriptors Diday discriminant dissimilarity measure distance domain elements empirical distribution function example extension factorial plane frequency distribution given histogram hypercube individual description vector interval variable logical dependence methods modal symbolic object modal variable multi-valued variable obtained partition predictors principal component analysis principal components probabilistic probability distribution query of type recursive partitioning relation relational databases representation rows Section similarity single-valued variable SODAS project SQL query strata subsets symbolic data analysis symbolic data array symbolic data table symbolic description symbolic variables terminal nodes training set V₁ variable Y variables Y₁ weight Y₁ Y₂ Zoom Star