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Subspace Clustering Method (I): Subspace Search Methods

• Search various subspaces to find clusters
• Bottom-up approaches
• Start from low-D subspaces and search higher-D subspaces only when there may be clusters in such subspaces
• Various pruning techniques to reduce the number of higher-D subspaces to be searched
• Ex. CLIQUE (Agrawal et al. 1998)
• Top-down approaches
• Start from full space and search smaller subspaces recursively
• Effective only if the locality assumption holds: restricts that the subspace of a cluster can be determined by the local neighborhood
• Ex. PROCLUS (Aggarwal et al. 1999): a k-medoid-like method