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New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping

Author

Listed:
  • Jean-Charles Lamirel

    (LORIA Vandoeuvre-lès-Nancy ()

  • Claire Francois

    (URI/INIST-CNRS Vandoeuvre-lès-Nancy ()

  • Shadi Al Shehabi

    (LORIA Vandoeuvre-lès-Nancy ()

  • Martial Hoffmann

    (URI/INIST-CNRS Vandoeuvre-lès-Nancy ()

Abstract

The information analysis process includes a cluster analysis or classification step associated with an expert validation of the results. In this paper, we propose new measures of Recall/Precision for estimating the quality of cluster analysis. These measures derive both from the Galois lattice theory and from the Information Retrieval (IR) domain. As opposed to classical measures of inertia, they present the main advantages to be both independent of the classification method and of the difference between the intrinsic dimension of the data and those of the clusters. We present two experiments on the basis of the MultiSOM model, which is an extension of Kohonen's SOM model, as a cluster analysis method. Our first experiment on patent data shows how our measures can be used to compare viewpoint-oriented classification methods, such as MultiSOM, with global cluster analysis method, such as WebSOM. Our second experiment, which takes part in the EICSTES EEC project, is an original Webometrics experiment that combines content and links classification starting from a large non-homogeneous set of web pages. This experiment highlights the fact that break-even points between our different measures of Recall/Precision can be used to determine an optimal number of clusters for web data classification. The content of the clusters obtained when using different break-even points are compared for determining the quality of the resulting maps.

Suggested Citation

  • Jean-Charles Lamirel & Claire Francois & Shadi Al Shehabi & Martial Hoffmann, 2004. "New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(3), pages 445-562, August.
    • Handle: RePEc:spr:scient:v:60:y:2004:i:3:d:10.1023_b:scie.0000034386.05278.e8
    DOI: 10.1023/B:SCIE.0000034386.05278.e8
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    References listed on IDEAS

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    1. S. E. Robertson & K. Sparck Jones, 1976. "Relevance weighting of search terms," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 27(3), pages 129-146, May.
    2. Laura A. Mather, 2000. "A linear algebra measure of cluster quality," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(7), pages 602-613.
    3. Xavier Polanco & Claire François & Jean-Charles Lamirel, 2001. "Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(1), pages 267-292, April.
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    Cited by:

    1. Jarneving, Bo, 2007. "Bibliographic coupling and its application to research-front and other core documents," Journal of Informetrics, Elsevier, vol. 1(4), pages 287-307.
    2. Jean-Charles Lamirel & Shadi Al Shehabi & Claire Francois & Xavier Polanco, 2004. "Using a compound approach based on elaborated neural network for Webometrics: An example issued from the EICSTES project," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(3), pages 427-441, November.
    3. Jean-Charles Lamirel, 2012. "A new approach for automatizing the analysis of research topics dynamics: application to optoelectronics research," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(1), pages 151-166, October.

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