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Document clustering in correlation similarity measure space

journal contribution
posted on 2012-06-01, 00:00 authored by T Zhang, Y Tang, B Fang, Yong XiangYong Xiang
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.

History

Journal

IEEE transactions on knowledge and data engineering

Volume

24

Issue

6

Pagination

1002 - 1013

Publisher

IEEE

Location

Piscataway, N. J.

ISSN

1041-4347

eISSN

1558-2191

Language

eng

Publication classification

C1 Refereed article in a scholarly journal