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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
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