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Scalable Construction of Topic Directory with Nonparametric Closed Termset Mining
Brighton, United Kingdom November 01-November 04
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10056Fourth IEEE International Conference ...
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Hwanjo Yu, University of Illinois at Urbana-Champaign
Duane Searsmith, University of Illinois at Urbana-Champaign
Xiaolei Li, University of Illinois at Urbana-Champaign
Jiawei Han, University of Illinois at Urbana-Champaign
A topic directory, e.g., Yahoo directory, provides a view of a document set at different levels of abstraction and is ideal for the interactive exploration and visualization of the document set. We present a method that dynamically generates a topic directory from a document set using a frequent closed termset mining algorithm. Our method shows experimental results of equal quality to recent document clustering methods and has additional benefits such as automatic generation of topic labels and determination of a clustering parameter.
Index Terms:
topic directory, document clustering, hierarchical clustering
Citation:
Hwanjo Yu, Duane Searsmith, Xiaolei Li, Jiawei Han, "Scalable Construction of Topic Directory with Nonparametric Closed Termset Mining," icdm, pp.563-566, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004
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