Mutual information based labelling and comparing clusters

Rob Koopman () and Shenghui Wang ()
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Rob Koopman: OCLC Research
Shenghui Wang: OCLC Research

Scientometrics, 2017, vol. 111, issue 2, No 29, 1157-1167

Abstract: 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 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s11192-017-2305-2

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