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Extraction de données symboliques et cartes topologiques: application aux données ayant une structure complexe

Aïcha El Golli 1
1 AxIS - Usage-centered design, analysis and improvement of information systems
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Paris-Rocquencourt
Abstract : The aim of symbolic data analysis is to provide a better representation of the variations and imprecision contained in real data. As such data express a higher level of knowledge; the representation must offer a richer formalism than that provided by classical data analysis. A generalization process exists that allows data to be synthesized and represented by means of an assertion formalism that was defined in symbolic data analysis. This generalization process is supervised and often sensitive to virtual and atypical individuals. When the data to be generalized is heterogeneous, some assertions include virtual individuals. Faced with this new formalism and the resulting semantic extension that symbolic data analysis offers, a new approach to processing and interpreting data is required. The original contributions of our work concern new approaches to representing and clustering complex data. First, we propose a decomposition step, based on a divisive clustering algorithm, that improves the generalization process while offering the symbolic formalism. We also propose a unsupervised generalization process based on the self-organizing map. The advantage of this method is that it enables the data to be reduced in an unsupervised way and allows the resulting homogeneous clusters to be represented by symbolic formalism. The second contribution of our work is a development of a clustering method to handle complex data. The method is an adaptation of the batch version of the self-organizing map to dissimilarity tables. Only the definition of an adequate dissimilarity is required for the method to operate efficiently.
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Contributor : Alain Monteil <>
Submitted on : Friday, October 12, 2007 - 2:20:58 PM
Last modification on : Friday, May 25, 2018 - 12:02:04 PM
Long-term archiving on: : Monday, September 24, 2012 - 1:21:48 PM


  • HAL Id : tel-00178900, version 1



Aïcha El Golli. Extraction de données symboliques et cartes topologiques: application aux données ayant une structure complexe. Interface homme-machine [cs.HC]. Université Paris Dauphine - Paris IX, 2004. Français. ⟨tel-00178900⟩



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