Scientometrics

, Volume 111, Issue 2, pp 1157–1167 | Cite as

Mutual information based labelling and comparing clusters

Article

Abstract

After a clustering solution is generated automatically, labelling these clusters becomes important to help understanding the results. In this paper, we propose to use a mutual information based method to label clusters of journal articles. Topical terms which have the highest normalised mutual information with a certain cluster are selected to be the labels of the cluster. Discussion of the labelling technique with a domain expert was used as a check that the labels are discriminating not only lexical-wise but also semantically. Based on a common set of topical terms, we also propose to generate lexical fingerprints as a representation of individual clusters. Eventually, we visualise and compare these fingerprints of different clusters from either one clustering solution or different ones.

Keywords

Cluster labelling Normalised mutual information Visualisation 

References

  1. Applegate, D. L., Bixby, R. E., Chvatal, V., & Cook, W. J. (2007). The traveling salesman problem: A computational study (Princeton series in applied mathematics). Princeton: Princeton University Press.Google Scholar
  2. Koopman, R., Wang, S., & Scharnhorst, A. (2017). Contextualization of topics—Browsing through the universe of bibliographic information. In J. Gläser, A. Scharnhorst, & W. Glänzel (Eds.), Same data—Different results?. Special issue of scientometrics: Towards a comparative approach to the identification of thematic structures in science. doi:10.1007/s11192-017-2303-4.
  3. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge: Cambridge University Press.CrossRefMATHGoogle Scholar
  4. Martin, O., Otto, S. W., & Felten, E. W. (1991). Large-step markov chains for the traveling salesman problem. Complex Systems, 5, 299–326.MathSciNetMATHGoogle Scholar
  5. Velden, T., Boyack, K., van Eck, N., Glänzel, W., Gläser, J., & Havemann, F., et al. (2017). Comparison of topic extraction approaches and their results. In J. Gläser, A. Scharnhorst, & W. Glänzel (Eds.), Same data—Different results?. Special issue of scientometrics: Towards a comparative approach to the identification of thematic structures in science. doi:10.1007/s11192-017-2306-1.
  6. Wang, S., & Koopman, R. (2017). Clustering articles based on semantic similarity. In J. Gläser, A. Scharnhorst, & W. Glänzel (Eds.), Same data–Different results?. Special issue of Scientometrics: Towards a comparative approach to the identification of thematic structures in science. doi:10.1007/s11192-017-2298-x.

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  1. 1.OCLC ResearchLeidenThe Netherlands

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