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dc.contributor.authorBudhaditya, S.
dc.contributor.authorPhung, D.
dc.contributor.authorPham, DucSon
dc.contributor.authorVenkatesh, S.
dc.contributor.editorM. Jaki, A. Siebes, J. Yu, B. Goethals, X. Wu
dc.identifier.citationBudhaditya, Saha and Phung, Dinh and Pham, Duc Son and Venkatesh, Svetha. 2012. Sparse subspace representation for spectral document clustering, in Zaki, M.J. and Siebes, A. and Yu, J.X. and Goethals, B. and Wu, X. (ed), 2012 IEEE 12th International Conference on Data Mining, Dec 10-13 2012, pp. 1902-1907. Brussels, Belgium: IEEE.

We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An ℓ1-norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to be part of a sub-group that shares a smaller set of similar vocabulary, thus allowing for cleaner clusters. Extensive experimental evaluations on two real-world datasets from Reuters-21578 and 20Newsgroup corpora show that our proposed method consistently outperforms state-of-the-art algorithms. Significantly, the performance improvement over other methods is prominent for this datasets.

dc.subjectdocument clustering
dc.subjectsparse representation
dc.titleSparse subspace representation for spectral document clustering
dc.typeConference Paper
dcterms.source.titleProceedings of the IEEE International Conference on Data Mining (ICDM)
dcterms.source.seriesProceedings of the IEEE International Conference on Data Mining (ICDM)
dcterms.source.conferenceThe IEEE International Conference on Data Mining
dcterms.source.conference-start-dateDec 10 2012
dcterms.source.conferencelocationBrussels, Belgium

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curtin.accessStatusOpen access

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