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

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
Externally published	Yes
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

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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).

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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",

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Document clustering via adaptive subspace iteration

Abstract

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Keywords

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