Abstract
In the recent years, several techniques have been developed for efficient similarity search in high-dimensional data spaces. Some of the techniques, based on the idea of vector approximation via quantization, have been shown to be the most effective. The VA-file was the first technique to use vector approximation. The IQ-tree and the A-tree are subsequent techniques that impose a directory structure over the quantized VA-file representation. The performance gains of the IQ-tree result mainly from an optimized I/O strategy permitted by the directory structure. Those of the A-tree result mainly from exploiting the clustering of the data itself. In our work, first we evaluate the relative performance of these two enhanced approaches over high-dimensional data sets with different clustering characteristics. Second, we present the Clustered IQ-Tree, which is an indexing strategy that combines the best features of the IQ-tree and the A-tree, leading to better query performance than the former and more stable performance than the latter across different types of data sets.
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Garcia-Arellano, C., Sevcik, K. (2003). Quantization Techniques for Similarity Search in High-Dimensional Data Spaces. In: James, A., Younas, M., Lings, B. (eds) New Horizons in Information Management. BNCOD 2003. Lecture Notes in Computer Science, vol 2712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45073-4_8
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DOI: https://doi.org/10.1007/3-540-45073-4_8
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