Issue No. 06 - June (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.49
Taiping Zhang , Chongqing University, Chongqing
Yuan Yan Tang , Chongqing University, Chongqing
Bin Fang , Chongqing University, Chongqing
Yong Xiang , Deakin University, Geelong
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.
Document clustering, correlation measure, correlation latent semantic indexing, dimensionality reduction.
Y. Xiang, T. Zhang, B. Fang and Y. Y. Tang, "Document Clustering in Correlation Similarity Measure Space," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 1002-1013, 2011.