Abstract
In this paper we present the Distal Spatial Approximation Tree (DiSAT), an algorithmic improvement of SAT. Our improvement increases the discarding power of the SAT by selecting distal nodes instead of the proximal nodes proposed in the original paper. Our approach is parameter free and it was the most competitive in an extensive benchmarking, from two to forty times faster than the SAT, and faster than the List of Clusters (LC) which is considered the state of the art for main memory, linear sized indexes in the model of distance computations.
In summary, we obtained an index more resistant to the curse of dimensionality, establishing a new benchmark in performance, faster to build than the LC and with a small memory footprint. Our strategies can be used in any version of the SAT, either in main or secondary memory.
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Chávez, E., Ludueña, V., Reyes, N., Roggero, P. (2014). Faster Proximity Searching with the Distal SAT. In: Traina, A.J.M., Traina, C., Cordeiro, R.L.F. (eds) Similarity Search and Applications. SISAP 2014. Lecture Notes in Computer Science, vol 8821. Springer, Cham. https://doi.org/10.1007/978-3-319-11988-5_6
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DOI: https://doi.org/10.1007/978-3-319-11988-5_6
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