Document clustering in correlation similarity measure space

Zhang, Taiping, Tang, Yuan Yan, Fang, Bin and Xiang, Yong 2012, Document clustering in correlation similarity measure space, IEEE transactions on knowledge and data engineering, vol. 24, no. 6, pp. 1002-1013.

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Title Document clustering in correlation similarity measure space
Author(s) Zhang, Taiping
Tang, Yuan Yan
Fang, Bin
Xiang, Yong
Journal name IEEE transactions on knowledge and data engineering
Volume number 24
Issue number 6
Start page 1002
End page 1013
Total pages 12
Publisher IEEE
Place of publication Piscataway, N. J.
Publication date 2012-06
ISSN 1041-4347
1558-2191
Keyword(s) correlation latent semantic indexing
correlation measure
dimensionality reduction
document clustering
Summary This paper presents a new spectral clustering method called correlation preserving indexing (CPI), which is performed in the correlation similarity measure space. In this framework, the documents are projected into a low-dimensional semantic space in which the correlations between the documents in the local patches are maximized while the correlations between the documents outside these patches are minimized simultaneously. Since the intrinsic geometrical structure of the document space is often embedded in the similarities between the documents, correlation as a similarity measure is more suitable for detecting the intrinsic geometrical structure of the document space than euclidean distance. Consequently, the proposed CPI method can effectively discover the intrinsic structures embedded in high-dimensional document space. The effectiveness of the new method is demonstrated by extensive experiments conducted on various data sets and by comparison with existing document clustering methods.
Language eng
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049642

Document type: Journal Article
Collection: School of Engineering
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