Abstract
The overwhelming amount of textual documents available nowadays highlights the need for information organization and discovery. Effectively organizing documents into a hierarchy of topics and subtopics makes it easier for users to browse the documents. This paper borrows community mining from social network analysis to generate a hierarchy of topically coherent document clusters. It focuses on giving the document clusters descriptive labels. We propose to use betweenness centrality measure in networks of co-occurring terms to label the clusters. We also incorporate keyphrase extraction and automatic titling in cluster labeling. The results show that the cluster labeling method utilizing KEA to extract keyphrases from the documents generates the best labels overall comparing to other methods and baselines.
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Li, X., Chen, J., Zaiane, O. (2013). Text Document Topical Recursive Clustering and Automatic Labeling of a Hierarchy of Document Clusters. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_17
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DOI: https://doi.org/10.1007/978-3-642-37456-2_17
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