The TV-tree -- an Index Structure for High-Dimensional Data
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We propose a file structure to index high-dimensionality data, typically, points in some feature space. The idea is to use only a few of the features, utilizing additional features whenever the additional discriminatory power is absolutely necessary. We present in detail the design of our tree structure and the associated algorithms that handle such 'varying length' feature vectors. Finally we report simulation results, comparing the proposed structure with the R*-tree, which is one of the most successful methods for low-dimensionality spaces. The results illustrate the superiority of our method, with up to 80% savings in disk accesses.