Document clustering via adaptive subspace iteration

Tao Li; Sheng Ma; Mitsunori Ogihara

doi:10.1145/1008992.1009031

Document clustering via adaptive subspace iteration

Tao Li, Sheng Ma, Mitsunori Ogihara

Computer Science

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

80 Scopus citations

Abstract

Document clustering has long been an important problem in information retrieval. In this paper, we present a new clustering algorithm ASI¹, which uses explicitly modeling of the subspace structure associated with each cluster. ASI simultaneously performs data reduction and subspace identification via an iterative alternating optimization procedure. Motivated from the optimization procedure, we then provide a novel method to determine the number of clusters. We also discuss the connections of ASI with various existential clustering approaches. Finally, extensive experimental results on real data sets show the effectiveness of ASI algorithm.

Original language	English (US)
Title of host publication	Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher	Association for Computing Machinery
Pages	218-225
Number of pages	8
ISBN (Print)	1581138814, 9781581138818
DOIs	https://doi.org/10.1145/1008992.1009031
State	Published - 2004
Event	Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - Sheffield, United Kingdom Duration: Jul 25 2004 → Jul 29 2004

Publication series

Name	Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other	Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Country	United Kingdom
City	Sheffield
Period	7/25/04 → 7/29/04

Keywords

Adaptive subspace identification
Alternating optimization
Document clustering
Factor analysis

ASJC Scopus subject areas

Engineering(all)

Access to Document

10.1145/1008992.1009031

Fingerprint Dive into the research topics of 'Document clustering via adaptive subspace iteration'. Together they form a unique fingerprint.

Cite this

Li, T., Ma, S., & Ogihara, M. (2004). Document clustering via adaptive subspace iteration. In Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 218-225). (Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval). Association for Computing Machinery. https://doi.org/10.1145/1008992.1009031

Document clustering via adaptive subspace iteration. / Li, Tao; Ma, Sheng; Ogihara, Mitsunori.

Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, 2004. p. 218-225 (Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval).

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

Li, T, Ma, S & Ogihara, M 2004, Document clustering via adaptive subspace iteration. in Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, pp. 218-225, Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Sheffield, United Kingdom, 7/25/04. https://doi.org/10.1145/1008992.1009031

Li T, Ma S, Ogihara M. Document clustering via adaptive subspace iteration. In Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery. 2004. p. 218-225. (Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval). https://doi.org/10.1145/1008992.1009031

Li, Tao ; Ma, Sheng ; Ogihara, Mitsunori. / Document clustering via adaptive subspace iteration. Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, 2004. pp. 218-225 (Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval).

@inproceedings{8e08897dece149928c005d6c3e47156e,

title = "Document clustering via adaptive subspace iteration",

abstract = "Document clustering has long been an important problem in information retrieval. In this paper, we present a new clustering algorithm ASI1, which uses explicitly modeling of the subspace structure associated with each cluster. ASI simultaneously performs data reduction and subspace identification via an iterative alternating optimization procedure. Motivated from the optimization procedure, we then provide a novel method to determine the number of clusters. We also discuss the connections of ASI with various existential clustering approaches. Finally, extensive experimental results on real data sets show the effectiveness of ASI algorithm.",

keywords = "Adaptive subspace identification, Alternating optimization, Document clustering, Factor analysis",

author = "Tao Li and Sheng Ma and Mitsunori Ogihara",

year = "2004",

doi = "10.1145/1008992.1009031",

language = "English (US)",

isbn = "1581138814",

series = "Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval",

publisher = "Association for Computing Machinery",

pages = "218--225",

booktitle = "Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval",

note = "Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ; Conference date: 25-07-2004 Through 29-07-2004",

}

TY - GEN

T1 - Document clustering via adaptive subspace iteration

AU - Li, Tao

AU - Ma, Sheng

AU - Ogihara, Mitsunori

PY - 2004

Y1 - 2004

N2 - Document clustering has long been an important problem in information retrieval. In this paper, we present a new clustering algorithm ASI1, which uses explicitly modeling of the subspace structure associated with each cluster. ASI simultaneously performs data reduction and subspace identification via an iterative alternating optimization procedure. Motivated from the optimization procedure, we then provide a novel method to determine the number of clusters. We also discuss the connections of ASI with various existential clustering approaches. Finally, extensive experimental results on real data sets show the effectiveness of ASI algorithm.

AB - Document clustering has long been an important problem in information retrieval. In this paper, we present a new clustering algorithm ASI1, which uses explicitly modeling of the subspace structure associated with each cluster. ASI simultaneously performs data reduction and subspace identification via an iterative alternating optimization procedure. Motivated from the optimization procedure, we then provide a novel method to determine the number of clusters. We also discuss the connections of ASI with various existential clustering approaches. Finally, extensive experimental results on real data sets show the effectiveness of ASI algorithm.

KW - Adaptive subspace identification

KW - Alternating optimization

KW - Document clustering

KW - Factor analysis

UR - http://www.scopus.com/inward/record.url?scp=8644250640&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=8644250640&partnerID=8YFLogxK

U2 - 10.1145/1008992.1009031

DO - 10.1145/1008992.1009031

M3 - Conference contribution

AN - SCOPUS:8644250640

SN - 1581138814

SN - 9781581138818

T3 - Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

SP - 218

EP - 225

BT - Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

PB - Association for Computing Machinery

T2 - Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Y2 - 25 July 2004 through 29 July 2004

ER -