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Adaptive Dimension Reduction for Clustering High Dimensional Data.

DE2003807420

Publication Date	2002
Personal Author	Ding, C.; He, X.; Zha, H.; Simon, H. D.
Page Count	12
Abstract	It is well-known that for high dimensional data clustering, standard algorithms such as EM and the K-means are often trapped in local minimum. Many initialization methods were proposed to tackle this problem, but with only limited success. In this paper we propose a new approach to resolve this problem by repeated dimension reductions such that K-means or EM are performed only in very low dimensions. Cluster membership is utilized as a bridge between the reduced dimensional subspace and the original space, providing flexibility and ease of implementation. Clustering analysis performed on highly overlapped Gaussians, DNA gene expression profiles and internet newsgroups demonstrate the effectiveness of the proposed algorithm.
Keywords	Algorithms Clustering Dimensions Iterations Attributes Classification Data processing Vectors Configuration Implementation
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Lawrence Berkeley National Lab., CA.; Pennsylvania State Univ., University Park.; Department of Energy, Washington, DC.
Supplemental Notes	Prepared in cooperation with Pennsylvania State Univ., University Park. Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200317

Adaptive Dimension Reduction for Clustering High Dimensional Data.

Adaptive Dimension Reduction for Clustering High Dimensional Data.
DE2003807420

Publication Date	2002
Personal Author	Ding, C.; He, X.; Zha, H.; Simon, H. D.
Page Count	12
Abstract	It is well-known that for high dimensional data clustering, standard algorithms such as EM and the K-means are often trapped in local minimum. Many initialization methods were proposed to tackle this problem, but with only limited success. In this paper we propose a new approach to resolve this problem by repeated dimension reductions such that K-means or EM are performed only in very low dimensions. Cluster membership is utilized as a bridge between the reduced dimensional subspace and the original space, providing flexibility and ease of implementation. Clustering analysis performed on highly overlapped Gaussians, DNA gene expression profiles and internet newsgroups demonstrate the effectiveness of the proposed algorithm.
Keywords	Algorithms Clustering Dimensions Iterations Attributes Classification Data processing Vectors Configuration Implementation
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Lawrence Berkeley National Lab., CA.; Pennsylvania State Univ., University Park.; Department of Energy, Washington, DC.
Supplemental Notes	Prepared in cooperation with Pennsylvania State Univ., University Park. Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200317