Mutual information based labelling and comparing clusters

Rob Koopman*, Shenghui Wang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1157-1167
Number of pages11
JournalScientometrics
Volume111
Issue number2
DOIs
Publication statusPublished - 1 May 2017
Externally publishedYes

Keywords

  • Cluster labelling
  • Normalised mutual information
  • Visualisation

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