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• W2106467368 abstract "Approaches to indexing and searching feature vectors are an indispensable factor to support similarity search effectively and efficiently. Such feature vectors extracted from real world objects are usually presented in the form of multidim ensional data. As a result, many multidimensional data index techniques have been widely introduced to the research community. These index techniques are categorized into two main classes: SP (space partitioning)/KD-tree-based and DP (data partitioning)/R-tree-based. Although there are a variety of mixed index techniques, which try to inherit positive aspects from more than one index technique, the number of techniques that are derived from these two main classes is just a few. In this paper, we introduce such a mixed index, the SH-tree: a novel and flexible super hybrid index structure for multidimensional data. Theoretical analyses indicate that the SH-tree is a good combination of the two index technique families with respect to both the presentation and search algorithms. It overcomes shortcomings and makes use of their positive aspects to facilitate efficient similarity searches in multidimensio nal data spaces. Empirical experiment results with both uniformly distributed and real data sets will confirm our theoretical analyses." @default.
• W2106467368 created "2016-06-24" @default.
• W2106467368 creator A5029072011 @default.
• W2106467368 date "2006-01-01" @default.
• W2106467368 modified "2023-10-16" @default.
• W2106467368 title "The SH-Tree: A Novel and Flexible Super Hybrid Index Structure for Similarity Search on Multidimensional Data †" @default.
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