Document clustering and cluster topic extraction in multilingual corpora

Joaquim Francisco Ferreira Silva; João Tiago Praça Nunes Mexia; Carlos A. Coelho; José Gabriel Pereira Lopes

Document clustering and cluster topic extraction in multilingual corpora

Joaquim Francisco Ferreira Silva, João Tiago Praça Nunes Mexia, Carlos A. Coelho, José Gabriel Pereira Lopes

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

Abstract

A statistics-based approach for clustering documentsand for extracting cluster topics is described. Relevant(meaningful} Expressions (RES} automatically extractedfrom corpora are used as clustering base features. Thesefeatures are transformed and its number is strongly reducedin order to obtain a small set of document classijication features. This is achieved on the basis of Principal Components Analysis. Model-Based Clustering Analysis finds thebest number of clusters. Then, the most important RES areextracted from each cluster and taken as document clustertopics.

Original language	English
Title of host publication	Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM 2001
Editors	N. Cercone, T. Y. Lin, X. Wu
Place of Publication	Los Alamitos, California
Publisher	IEEE Computer Society
Pages	513-520
Number of pages	8
ISBN (Print)	978-0-7695-1119-8
Publication status	Published - 1 Jan 2001
Event	2001 IEEE International Conference on Data Mining - San Jose, United States Duration: 29 Nov 2001 → 2 Dec 2001

Conference

Conference	2001 IEEE International Conference on Data Mining
Country/Territory	United States
City	San Jose
Period	29/11/01 → 2/12/01

Keywords

Information retrieval systems
Principal component analysis
Data mining

Cite this

@inproceedings{8f692e5113c643219a3a84449d2cc2a1,

title = "Document clustering and cluster topic extraction in multilingual corpora",

abstract = "A statistics-based approach for clustering documentsand for extracting cluster topics is described. Relevant(meaningful} Expressions (RES} automatically extractedfrom corpora are used as clustering base features. Thesefeatures are transformed and its number is strongly reducedin order to obtain a small set of document classijication features. This is achieved on the basis of Principal Components Analysis. Model-Based Clustering Analysis finds thebest number of clusters. Then, the most important RES areextracted from each cluster and taken as document clustertopics. ",

keywords = "Information retrieval systems, Principal component analysis, Data mining",

author = "Silva, {Joaquim Francisco Ferreira} and Mexia, {Jo{\~a}o Tiago Pra{\c c}a Nunes} and Coelho, {Carlos A.} and Lopes, {Jos{\'e} Gabriel Pereira}",

year = "2001",

month = jan,

day = "1",

language = "English",

isbn = "978-0-7695-1119-8",

pages = "513--520",

editor = "N. Cercone and Lin, {T. Y.} and X. Wu",

booktitle = "Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM 2001",

publisher = "IEEE Computer Society",

address = "United States",

note = "2001 IEEE International Conference on Data Mining ; Conference date: 29-11-2001 Through 02-12-2001",

}

Silva, JFF , Mexia, JTPN , Coelho, CA & Lopes, JGP 2001, Document clustering and cluster topic extraction in multilingual corpora. in N Cercone, TY Lin & X Wu (eds), Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM 2001. IEEE Computer Society, Los Alamitos, California, pp. 513-520, 2001 IEEE International Conference on Data Mining, San Jose, California, United States, 29/11/01.

Document clustering and cluster topic extraction in multilingual corpora. / Silva, Joaquim Francisco Ferreira ; Mexia, João Tiago Praça Nunes ; Coelho, Carlos A. et al.
Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM 2001. ed. / N. Cercone; T. Y. Lin; X. Wu. Los Alamitos, California: IEEE Computer Society, 2001. p. 513-520.

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

TY - GEN

T1 - Document clustering and cluster topic extraction in multilingual corpora

AU - Silva, Joaquim Francisco Ferreira

AU - Mexia, João Tiago Praça Nunes

AU - Coelho, Carlos A.

AU - Lopes, José Gabriel Pereira

PY - 2001/1/1

Y1 - 2001/1/1

N2 - A statistics-based approach for clustering documentsand for extracting cluster topics is described. Relevant(meaningful} Expressions (RES} automatically extractedfrom corpora are used as clustering base features. Thesefeatures are transformed and its number is strongly reducedin order to obtain a small set of document classijication features. This is achieved on the basis of Principal Components Analysis. Model-Based Clustering Analysis finds thebest number of clusters. Then, the most important RES areextracted from each cluster and taken as document clustertopics.

AB - A statistics-based approach for clustering documentsand for extracting cluster topics is described. Relevant(meaningful} Expressions (RES} automatically extractedfrom corpora are used as clustering base features. Thesefeatures are transformed and its number is strongly reducedin order to obtain a small set of document classijication features. This is achieved on the basis of Principal Components Analysis. Model-Based Clustering Analysis finds thebest number of clusters. Then, the most important RES areextracted from each cluster and taken as document clustertopics.

KW - Information retrieval systems

KW - Principal component analysis

KW - Data mining

M3 - Conference contribution

SN - 978-0-7695-1119-8

SP - 513

EP - 520

BT - Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM 2001

A2 - Cercone, N.

A2 - Lin, T. Y.

A2 - Wu, X.

PB - IEEE Computer Society

CY - Los Alamitos, California

T2 - 2001 IEEE International Conference on Data Mining

Y2 - 29 November 2001 through 2 December 2001

ER -

Document clustering and cluster topic extraction in multilingual corpora

Abstract

Conference

Keywords

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