Scalable Construction of Topic Directory with Nonparametric Closed Termset Mining

Authors:
Hwanjo Yu;Duane Searsmith;Xiaolei Li;Jiawei Han
Affiliations:
University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

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Abstract

A topic directory, e.g., Yahoo directory, provides a view of a document set at different levelsof 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 usinga 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.