Adapting spectral co-clustering to documents and terms using Latent Semantic Analysis

Laurence A.F. Park; Christopher A. Leckie; Kotagiri Ramamohanarao; James C. Bezdek

doi:10.1007/978-3-642-10439-8_31

Adapting spectral co-clustering to documents and terms using Latent Semantic Analysis

Laurence A.F. Park, Christopher A. Leckie, Kotagiri Ramamohanarao, James C. Bezdek

University of Melbourne

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

8 Citations (Scopus)

Abstract

Spectral co-clustering is a generic method of computing coclusters of relational data, such as sets of documents and their terms. Latent semantic analysis is a method of document and term smoothing that can assist in the information retrieval process. In this article we examine the process behind spectral clustering for documents and terms, and compare it to Latent Semantic Analysis. We show that both spectral co-clustering and LSA follow the same process, using different normalisation schemes and metrics. By combining the properties of the two co-clustering methods, we obtain an improved co-clustering method for document-term relational data that provides an increase in the cluster quality of 33.0%.

Original language	English
Title of host publication	AI 2009
Subtitle of host publication	Advances in Artificial Intelligence - 22nd Australasian Joint Conference, Proceedings
Pages	301-311
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-642-10439-8_31
Publication status	Published - 2009
Externally published	Yes
Event	22nd Australasian Joint Conference on Artificial Intelligence, AI 2009 - Melbourne, VIC, Australia Duration: 1 Dec 2009 → 1 Dec 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5866 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd Australasian Joint Conference on Artificial Intelligence, AI 2009
Country/Territory	Australia
City	Melbourne, VIC
Period	1/12/09 → 1/12/09

Keywords

Co-clustering
Document clustering
Latent semantic analysis
Spectral graph partitioning

Access to Document

10.1007/978-3-642-10439-8_31

Cite this

Park, L. A. F., Leckie, C. A., Ramamohanarao, K., & Bezdek, J. C. (2009). Adapting spectral co-clustering to documents and terms using Latent Semantic Analysis. In AI 2009: Advances in Artificial Intelligence - 22nd Australasian Joint Conference, Proceedings (pp. 301-311). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5866 LNAI). https://doi.org/10.1007/978-3-642-10439-8_31

Park, Laurence A.F. ; Leckie, Christopher A. ; Ramamohanarao, Kotagiri et al. / Adapting spectral co-clustering to documents and terms using Latent Semantic Analysis. AI 2009: Advances in Artificial Intelligence - 22nd Australasian Joint Conference, Proceedings. 2009. pp. 301-311 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Spectral co-clustering is a generic method of computing coclusters of relational data, such as sets of documents and their terms. Latent semantic analysis is a method of document and term smoothing that can assist in the information retrieval process. In this article we examine the process behind spectral clustering for documents and terms, and compare it to Latent Semantic Analysis. We show that both spectral co-clustering and LSA follow the same process, using different normalisation schemes and metrics. By combining the properties of the two co-clustering methods, we obtain an improved co-clustering method for document-term relational data that provides an increase in the cluster quality of 33.0%.",

keywords = "Co-clustering, Document clustering, Latent semantic analysis, Spectral graph partitioning",

author = "Park, {Laurence A.F.} and Leckie, {Christopher A.} and Kotagiri Ramamohanarao and Bezdek, {James C.}",

year = "2009",

doi = "10.1007/978-3-642-10439-8_31",

language = "English",

isbn = "364210438X",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "AI 2009",

note = "22nd Australasian Joint Conference on Artificial Intelligence, AI 2009 ; Conference date: 01-12-2009 Through 01-12-2009",

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Park, LAF, Leckie, CA, Ramamohanarao, K & Bezdek, JC 2009, Adapting spectral co-clustering to documents and terms using Latent Semantic Analysis. in AI 2009: Advances in Artificial Intelligence - 22nd Australasian Joint Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5866 LNAI, pp. 301-311, 22nd Australasian Joint Conference on Artificial Intelligence, AI 2009, Melbourne, VIC, Australia, 1/12/09. https://doi.org/10.1007/978-3-642-10439-8_31

Adapting spectral co-clustering to documents and terms using Latent Semantic Analysis. / Park, Laurence A.F.; Leckie, Christopher A.; Ramamohanarao, Kotagiri et al.
AI 2009: Advances in Artificial Intelligence - 22nd Australasian Joint Conference, Proceedings. 2009. p. 301-311 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5866 LNAI).

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

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T1 - Adapting spectral co-clustering to documents and terms using Latent Semantic Analysis

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AU - Leckie, Christopher A.

AU - Ramamohanarao, Kotagiri

AU - Bezdek, James C.

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AB - Spectral co-clustering is a generic method of computing coclusters of relational data, such as sets of documents and their terms. Latent semantic analysis is a method of document and term smoothing that can assist in the information retrieval process. In this article we examine the process behind spectral clustering for documents and terms, and compare it to Latent Semantic Analysis. We show that both spectral co-clustering and LSA follow the same process, using different normalisation schemes and metrics. By combining the properties of the two co-clustering methods, we obtain an improved co-clustering method for document-term relational data that provides an increase in the cluster quality of 33.0%.

KW - Co-clustering

KW - Document clustering

KW - Latent semantic analysis

KW - Spectral graph partitioning

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DO - 10.1007/978-3-642-10439-8_31

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Park LAF, Leckie CA, Ramamohanarao K, Bezdek JC. Adapting spectral co-clustering to documents and terms using Latent Semantic Analysis. In AI 2009: Advances in Artificial Intelligence - 22nd Australasian Joint Conference, Proceedings. 2009. p. 301-311. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-10439-8_31

Adapting spectral co-clustering to documents and terms using Latent Semantic Analysis

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