Text Document Topical Recursive Clustering and Automatic Labeling of a Hierarchy of Document Clusters

  • Xiaoxiao Li
  • Jiyang Chen
  • Osmar Zaiane
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)


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.


Text Mining and Web Mining Cluster Labeling Document Clustering 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaoxiao Li
    • 1
  • Jiyang Chen
    • 2
  • Osmar Zaiane
    • 1
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  2. 2.Google CanadaKitchenerCanada

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