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

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 proceeding › Conference contribution

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 language	English (US)
Title of host publication	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages	2384-2389
Number of pages	6
State	Published - Dec 12 2005
Externally published	Yes
Event	International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: Aug 18 2005 → Aug 21 2005

Publication series

Name	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Other

Other	International Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/Territory	China
City	Guangzhou
Period	8/18/05 → 8/21/05

Keywords

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

ASJC Scopus subject areas

General Engineering

Cite this

Application of LSA space's dimension character in document multi-hierarchy clustering. / Liu, Yun Feng; Qi, Huan; Hu, Xiang En et al.
2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005. p. 2384-2389 (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Liu, YF, Qi, H, Hu, XE, Cai, ZQ, Dai, JM & Zhu, L 2005, Application of LSA space's dimension character in document multi-hierarchy clustering. in 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005, pp. 2384-2389, International Conference on Machine Learning and Cybernetics, ICMLC 2005, Guangzhou, China, 8/18/05.

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title = "Application of LSA space's dimension character in document multi-hierarchy clustering",

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.",

keywords = "Concept Granularity, Document Multi-hierarchy Clustering, Document Self-indexing Matrix, Latent Semantic Analysis",

author = "Liu, {Yun Feng} and Huan Qi and Hu, {Xiang En} and Cai, {Zhi Qiang} and Dai, {Jian Min} and Li Zhu",

year = "2005",

month = dec,

day = "12",

language = "English (US)",

isbn = "078039092X",

series = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

pages = "2384--2389",

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note = "International Conference on Machine Learning and Cybernetics, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",

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T1 - Application of LSA space's dimension character in document multi-hierarchy clustering

AU - Liu, Yun Feng

AU - Qi, Huan

AU - Hu, Xiang En

AU - Cai, Zhi Qiang

AU - Dai, Jian Min

AU - Zhu, Li

PY - 2005/12/12

Y1 - 2005/12/12

N2 - 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.

AB - 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.

KW - Concept Granularity

KW - Document Multi-hierarchy Clustering

KW - Document Self-indexing Matrix

KW - Latent Semantic Analysis

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M3 - Conference contribution

AN - SCOPUS:28444478040

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SN - 9780780390928

T3 - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

SP - 2384

EP - 2389

BT - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

T2 - International Conference on Machine Learning and Cybernetics, ICMLC 2005

Y2 - 18 August 2005 through 21 August 2005

ER -

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

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

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