When analyzing a document collection, a key piece of information is the number of distinct topics it contains. Document clustering has been used as a tool to facilitate the extraction of such information. However, existing clustering methods do not take into account the sequences of the words in the documents, and usually do not have the means to describe the contents within each topic cluster. In this paper, we record our investigation and results using Maximal Frequent word Sequences (MFSs) as building blocks in identifying distinct topics. The supporting documents of MFSs are grouped into an equivalence class and then linked to a topic cluster, and the MFSs serve as the document cluster identifier. We describe the original method in extracting the set of MFSs, and how it can be adapted to identify topics in a textual dataset. We also demonstrate how the MFSs themselves can act as topic descriptors for the clusters. Finally, the benchmarking study with other existing clustering methods, i.e. k-Means and EM algorithm, shows the effectiveness of our approach for topic detection.


Equivalence Class Association Rule Expectation Maximization Expectation Maximization Algorithm Document Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ivan Yap
    • 1
  • Han Tong Loh
    • 1
  • Lixiang Shen
    • 2
  • Ying Liu
    • 3
  1. 1.Department of Mechanical Engineering, Blk EA 07-08National University of SingaporeSingapore
  2. 2.Design Technology Institute Ltd, Faculty of Engineering, Blk E4 01-07National University of SingaporeSingapore
  3. 3.Singapore MIT Alliance, E4-04-10National University of SingaporeSingapore

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