Scalable construction of topic directory with nonparametric closed termset mining

Hwanjo Yu, Duane Searsmith, Xiaolei Li, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
EditorsR. Rastogi, K. Morik, M. Bramer, X. Wu
Pages563-566
Number of pages4
StatePublished - 2004
EventProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom
Duration: Nov 1 2004Nov 4 2004

Publication series

NameProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Other

OtherProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
Country/TerritoryUnited Kingdom
CityBrighton
Period11/1/0411/4/04

Keywords

  • Document clustering
  • Hierarchical clustering
  • Topic directory

ASJC Scopus subject areas

  • Engineering(all)

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