IDEAS home Printed from https://ideas.repec.org/a/bla/jamist/v60y2009i8p1635-1651.html
   My bibliography  Save this article

How to normalize cooccurrence data? An analysis of some well‐known similarity measures

Author

Listed:
  • Nees Jan van Eck
  • Ludo Waltman

Abstract

In scientometric research, the use of cooccurrence data is very common. In many cases, a similarity measure is employed to normalize the data. However, there is no consensus among researchers on which similarity measure is most appropriate for normalization purposes. In this article, we theoretically analyze the properties of similarity measures for cooccurrence data, focusing in particular on four well‐known measures: the association strength, the cosine, the inclusion index, and the Jaccard index. We also study the behavior of these measures empirically. Our analysis reveals that there exist two fundamentally different types of similarity measures, namely, set‐theoretic measures and probabilistic measures. The association strength is a probabilistic measure, while the cosine, the inclusion index, and the Jaccard index are set‐theoretic measures. Both our theoretical and our empirical results indicate that cooccurrence data can best be normalized using a probabilistic measure. This provides strong support for the use of the association strength in scientometric research.

Suggested Citation

  • Nees Jan van Eck & Ludo Waltman, 2009. "How to normalize cooccurrence data? An analysis of some well‐known similarity measures," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(8), pages 1635-1651, August.
    • Handle: RePEc:bla:jamist:v:60:y:2009:i:8:p:1635-1651
    DOI: 10.1002/asi.21075
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.21075
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asi.21075?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jamist:v:60:y:2009:i:8:p:1635-1651. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.asis.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.