Novel Labeling Strategies for Hierarchical Representation of Multidimensional Data Analysis Results

Jean-Charles Lamirel 1 Anh Phuong Ta Mohammed Attik 2, 3
1 TALARIS - Natural Language Processing: representation, inference and semantics
Inria Nancy - Grand Est, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Hyperbolic visualization represents a useful tool for the interpretation of complex data analysis results, whenever it can be combined with efficient labeling strategies. In this paper, we firstly present a new approach combining original hypertree construction techniques for multidimensional clustering results visualization with novel cluster labeling techniques based on the use of cluster content evaluation criteria, like the F-measure on cluster properties. The first part of the paper briefly presents the cluster hypertree construction principle. The main part of the paper focuses on the presentation of the labeling techniques. It illustrates that the scope of the proposed techniques can be extended from single cluster labeling to labeling of hierarchical structures, like hypertrees. Finally, using specific evaluation criteria, we show the better efficiency of the proposed methods, as compared to usual labeling methods, both for single cluster labeling and for hierarchical labeling. The experimental context of the paper is a bibliographic database of 2127 PASCAL references related to the geological domain.
Type de document :
Communication dans un congrès
AIA - IASTED, Feb 2008, Innbruck, Austria. 2008
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https://hal.inria.fr/inria-00337304
Contributeur : Jean-Charles Lamirel <>
Soumis le : jeudi 6 novembre 2008 - 15:49:02
Dernière modification le : jeudi 11 janvier 2018 - 06:21:35

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  • HAL Id : inria-00337304, version 1

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Jean-Charles Lamirel, Anh Phuong Ta, Mohammed Attik. Novel Labeling Strategies for Hierarchical Representation of Multidimensional Data Analysis Results. AIA - IASTED, Feb 2008, Innbruck, Austria. 2008. 〈inria-00337304〉

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