  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
(NMI) 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.
