Clustering of High-dimensional Data using...

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We propose a fast method for clustering high-dimensional data.  Our proposed method is a top-down hierarchical clustering method using a binary tree of 1D random projections; at each stage, the data is partitioned into two based on the binary clustering found in a 1D random projection of the data. Our approach is efficient because most of the computations are performed in 1D. 

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