• xmlui.mirage2.page-structure.header.title
    • français
    • English
  • Help
  • Login
  • Language 
    • Français
    • English
View Item 
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

BIRDResearch centres & CollectionsBy Issue DateAuthorsTitlesTypeThis CollectionBy Issue DateAuthorsTitlesType

My Account

LoginRegister

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors
Thumbnail - Request a copy

From the statistics of data to the statistics of knowledge: Symbolic data analysis.

Billard, Lynne; Diday, Edwin (2003), From the statistics of data to the statistics of knowledge: Symbolic data analysis., Journal of the American Statistical Association, 98, 462, p. 470-487

Type
Article accepté pour publication ou publié
Date
2003
Journal name
Journal of the American Statistical Association
Volume
98
Number
462
Publisher
Taylor & Francis
Pages
470-487
Metadata
Show full item record
Author(s)
Billard, Lynne
Diday, Edwin
Abstract (EN)
Increasingly, datasets are so large they must be summarized in some fashion so that the resulting summary dataset is of a more manageable size, while still retaining as much knowledge inherent to the entire dataset as possible. One consequence of this situation is that the data may no longer be formatted as single values such as is the case for classical data, but rather may be represented by lists, intervals, distributions, and the like. These summarized data are examples of symbolic data. This article looks at the concept of symbolic data in general, and then attempts to review the methods currently available to analyze such data. It quickly becomes clear that the range of methodologies available draws analogies with developments before 1900 that formed a foundation for the inferential statistics of the 1900s, methods largely limited to small (by comparison) datasets and classical data formats. The scarcity of available methodologies for symbolic data also becomes clear and so draws attention to an enormous need for the development of a vast catalog (so to speak) of new symbolic methodologies along with rigorous mathematical and statistical foundational work for these methods.
Subjects / Keywords
Statistiques; Analyse des données

Related items

Showing items related by title and author.

  • Thumbnail
    Symbolic Data Analysis : Conceptual statistics and data Mining 
    Diday, Edwin; Billard, Lynne (2006-01) Chapitre d'ouvrage
  • Thumbnail
    Symbolic Data Analysis: Definition and Examples 
    Billard, Lynne; Diday, Edwin (2004) Document de travail / Working paper
  • Thumbnail
    Descriptive Statistics for Interval-valued Observations in the presence of Rules 
    Billard, Lynne; Diday, Edwin (2006-01) Article accepté pour publication ou publié
  • Thumbnail
    Clustering Methodology for Symbolic Data 
    Billard, Lynne; Diday, Edwin (2019) Ouvrage
  • Thumbnail
    From the Symbolic Analysis of Virtual Faces to a Smiles Machine 
    Ochs, Magalie; Diday, Edwin; Afonso, Filipe (2016) Article accepté pour publication ou publié
Dauphine PSL Bibliothèque logo
Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16
Phone: 01 44 05 40 94
Contact
Dauphine PSL logoEQUIS logoCreative Commons logo