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Shrinking data balls in metric indexes
unpublished
Some of the existing techniques for approximate similarity retrieval in metric spaces are focused on shrinking the query region by user-defined parameter. We modify this approach slightly and present a new approximation technique that shrinks data regions instead. The proposed technique can be applied to any metric indexing structure based on the ball-partitioning principle. Experiments show that our technique performs better than the relative error approximation and region proximity
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