Abstract
The permutation based index has shown to be very effective in medium and high dimensional metric spaces, even in difficult problems such as solving reverse k-nearest neighbor queries. Nevertheless, currently there is no study about which are the desirable features one can ask to a permutant set, or how to select good permutants. Similar to the case of pivots, our experimental results show that, compared with a randomly chosen set, a good permutant set yields to fast query response or to reduce the amount of space used by the index. In this paper, we start by characterizing permutants and studying their predictive power; then we propose an effective heuristic to select a good set of permutant candidates. We also show empirical evidence that supports our technique.
This work is partially funded by National Council of Science and Technology (CONACyT) of México, Universidad Michoacana de San Nicolás de Hidalgo, México, and Fondecyt grant 1131044, Chile.
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Figueroa, K., Paredes, R. (2014). An Effective Permutant Selection Heuristic for Proximity Searching in Metric Spaces. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-Lopez, J.A., Salas-Rodríguez, J., Suen, C.Y. (eds) Pattern Recognition. MCPR 2014. Lecture Notes in Computer Science, vol 8495. Springer, Cham. https://doi.org/10.1007/978-3-319-07491-7_11
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DOI: https://doi.org/10.1007/978-3-319-07491-7_11
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