Mutual Information Based Labelling and Comparing Clusters

by: Rob Koopman and Shenghui Wang

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.

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Suggested Citation:

Koopman, R. & Wang, S. 2017. Mutual Information Based Labelling and Comparing Clusters. Scientometrics 111: 1157.