Application of LSA space's dimension character in document multi-hierarchy clustering

Yun Feng Liu, Huan Qi, Xiang En Hu, Zhi Qiang Cai, Jian Min Dai, Li Zhu

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

1 Scopus citations

Abstract

In LSA space, dimensions corresponding to bigger singular values reflect the general concept of language elements, while dimensions corresponding to smaller singular values reflect particular concept of language elements. On this basis, different dimensions of LSA space are adopted for document clustering under various concept granularities. In addition, in the LSA-based algorithm of document clustering, better clustering results are obtained by taking the row vectors of document self-indexing matrix as the objects to be clustered, instead of the document vectors with low dimensionality.

Original languageEnglish (US)
Title of host publication2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages2384-2389
Number of pages6
StatePublished - Dec 12 2005
Externally publishedYes
EventInternational Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China
Duration: Aug 18 2005Aug 21 2005

Publication series

Name2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Other

OtherInternational Conference on Machine Learning and Cybernetics, ICMLC 2005
CountryChina
CityGuangzhou
Period8/18/058/21/05

Keywords

  • Concept Granularity
  • Document Multi-hierarchy Clustering
  • Document Self-indexing Matrix
  • Latent Semantic Analysis

ASJC Scopus subject areas

  • Engineering(all)

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