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2002
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Adaptive dimension reduction for clustering high dimensional data

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Adaptive dimension reduction for clustering high dimensional data
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.
Chris H. Q. Ding, Xiaofeng He, Hongyuan Zha, Horst
Added 14 Jul 2010
Updated 14 Jul 2010
Type Conference
Year 2002
Where ICDM
Authors Chris H. Q. Ding, Xiaofeng He, Hongyuan Zha, Horst D. Simon
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