Symbolic Data Analysis and the SODAS Software
(Sprache: Englisch)
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...
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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.
Klappentext zu „Symbolic Data Analysis and the SODAS Software “
Classical statistical techniques are often inadequate when it comes to analysing some of the large and internally variable datasets common today. Symbolic Data Analysis (SDA) has evolved in response to this problem and is a vital tool for summarizing information in such a way that the resulting data is of a manageable size. Symbolic data, represented byintervals, lists, histograms, distributions, curves and the like, keeps the "internal variation" of summaries better than standard data. SDA therefore plays a key role in the interaction between statistics and data processing, and has established itself as an important tool for analysing official statistics.
Through an extension of the concepts employed in data mining, the Editors provide an advanced guide to the techniques required to analyse symbolic data. Contributions from leading experts in the field enable the reader to build models and make predictions about future events.
The book:
* Provides new graphical tools for the interpretation of large data sets.
* Extends standard statistics, data analysis, data mining and knowledge discovery to symbolic data.
* Introduces the SODAS software, which is complementary to existing data analysis software (e.g. SAS, SPSS, SPAD) that are unable to work on symbolic data.
* Induces, exports, and compares knowledge from one database to another.
* Features a supporting website hosting the software, and user manual.
Symbolic Data Analysis and the SODAS Software is primarily aimed at practitioners of symbolic data analysis, such as statisticians and economists, within both the public and private sectors. There is also much of interest to postgraduate students and researchers within web mining, text mining, and bioengineering.
Inhaltsverzeichnis zu „Symbolic Data Analysis and the SODAS Software “
- Contributors- Foreword
- Preface
- Introduction
1. The state of the art in symbolic data analysis: overview and future (Edwin Diday and Monique-Noirhomme-Fraiture)
PART I. DATABASES VERSUS SYMBOLIC OBJECTS
2. Improved generation of symbolic objects from relational databases (Yves Lechevallier, Aicha El Golli and George Hébrail)
3. Exporting symbolic objects to databases (Donato Malerba, Floriana Esposito and Annalisa Appice)
4. A statistical metadata model for symbolic objects (H. Papageorgiou and Maria Vardaki)
5. Editing symbolic data (Monique-Noirhomme-Fraiture, Paula Brito, Anne de Baenst-Vandenbroucke and A. Nahimana)
6. The normal symbolic form (M. Csernel and Francisco de A.T. de Carvalho)
7. Visualization (Monique-Noirhomme-Fraiture and A. Nahimana)
PART II. UNSUPERVISED METHODS
8. Dissimilarity and matching (Floriana Esposito, Donato Malerba and Annalisa Appice)
9. Unsupervised divisive classification (Jean-Paul Rasson, Jean-Yves Pirçon, Pascale Lallemand and S,verine Adans)
10. Hierarchical and pyramidal clustering (Paula Brito and Francisco de A.T. de Carvalho)
11..Clustering methods in symbolic data analysis (Francisco de A.T. de Carvalho, Yves Lechevallier and Rosanna Verde)
12. Visualizing symbolic data by Kohonen maps (Hans-Hermann Bock)
13..Validation of clustering structure: determination of the number of clusters (André Hardy)
14. Stability measures for assessing a partition and its clusters: application to symbolic data sets (P. Bertrand and G. Bel Mufti)
15. Principal component analysis of symbolic data described by intervals (N.C. Lauro, Rosanna Verde and A. Irpino)
16. Generalized canonical analysis (N.C. Lauro, Rosanna Verde and A. Irpino)
PART III .SUPERVISED METHODS
17. Bayesian decision trees (Jean-Paul Rasson, Pascale Lallemand and Séverine Adans)
18. Factor discriminant analysis (N.C. Lauro, Rosanna Verde and A. Irpino)
19. Symbolic linear regression methodology
... mehr
(Filipe Afonso, Lynne Billard, Edwin Diday and Mehdi Limam)
20. Multi-layer perceptrons and symbolic data (Fabrice Rossi and Brieuc Conan-Guez)
PART IV. APPLICATION AND THE SODAS SOFTWARE
21. Application to the Finnish, Spanish and Portuguese data of the European Social Survey (Soile Mustjärvi and Seppo Laaksonen)
22. People's life values and trust components in Europe: symbolic data analysis for 20-22 countries (Seppo Laaksonen)
23. Symbolic analysis of the Time Use Survey in the Basque country (M. Mas and H. Olaeta)
24. SODAS2 software: overview and methodology (Anne de Baenst-Vandenbroucke and Yves Lechevallier)
- Index
20. Multi-layer perceptrons and symbolic data (Fabrice Rossi and Brieuc Conan-Guez)
PART IV. APPLICATION AND THE SODAS SOFTWARE
21. Application to the Finnish, Spanish and Portuguese data of the European Social Survey (Soile Mustjärvi and Seppo Laaksonen)
22. People's life values and trust components in Europe: symbolic data analysis for 20-22 countries (Seppo Laaksonen)
23. Symbolic analysis of the Time Use Survey in the Basque country (M. Mas and H. Olaeta)
24. SODAS2 software: overview and methodology (Anne de Baenst-Vandenbroucke and Yves Lechevallier)
- Index
... weniger
Autoren-Porträt von Diday, Noirhomme-Frait
Edwin Diday , Centre De Recherche en Mathématiques de la Décision, Université Paris 9, France Edwin is a Professor of Computer Science, with 50 published papers, and 14 authored or edited books to his name. He has led international research teams in Symbolic Data Analysis, and is the founder of the field. M. Noirhomme Fraiture , Institute of Computer Science, University of Namur, Belgium Monique Noirhomme Fraiture is Professor and Head of the Unit of Applied Mathematics at the above faculty. She is involved in several HCI projects as well as having organized conferences and workshops within this field. She has contributed to 28 published papers and co authored 2 books.
Bibliographische Angaben
- Autoren: Diday , Noirhomme-Frait
- 2007, 1. Auflage, 416 Seiten, Maße: 17,5 x 25 cm, Gebunden, Englisch
- Herausgegeben: Edwin Diday, Monique Noirhomme-Fraiture
- Verlag: Wiley & Sons
- ISBN-10: 0470018836
- ISBN-13: 9780470018835
- Erscheinungsdatum: 12.03.2008
Sprache:
Englisch
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