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Cluster labeling
In natural language processing and information retrieval, cluster labeling is the problem of picking descriptive, human-readable labels for the clusters produced by a document clustering algorithm; standard clustering algorithms do not typically produce any such labels. Cluster labeling algorithms examine the contents of the documents per cluster to find a labeling that summarize the topic of each cluster and distinguish the clusters from each other.
==Differential cluster labeling==
Differential cluster labeling labels a cluster by comparing term distributions across clusters, using techniques also used for feature selection in document classification, such as mutual information and chi-squared feature selection. Terms having very low frequency are not the best in representing the whole cluster and can be omitted in labeling a cluster. By omitting those rare terms and using a differential test, one can achieve the best results with differential cluster labeling.〔Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schutze. ''Introduction to Information Retrieval''. Cambridge: Cambridge UP, 2008. ''Cluster Labeling''. Stanford Natural Language Processing Group. Web. 25 Nov. 2009. .〕
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