Abstract
Efficient searching of the k-nearest neighbors (k-NN) is a widely discussed problem. Most of the known 2D methods is based on division of a space to some quads or rectangular clusters. It is convenient for simple orthogonal querying of the space. However, a radius of neighbourhood is circular, thus the non complying quads have to be eliminated. This paper describes a novel approach of searching k-NN using hexagonal clustering of the 2D unordered point clouds. The hexagonal grid fully fills the 2D space as well. The shape of a hexagon is closer to the circular one and hexagonal coordinate systems are efficiently used to simply address the surrounding hexagons intersected by neighbourhood of a point. The paper contains performance tests of the proposed algorithm.
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Acknowledgments
This work was supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme and by Project SP2015/146 “Parallel processing of Big data 2” of the Student Grand System, VŠB—Technical University of Ostrava.
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Uher, V., Gajdoš, P., Ježowicz, T., Snášel, V. (2016). Application of Hexagonal Coordinate Systems for Searching the K-NN in 2D Space. In: Snášel, V., Abraham, A., Krömer, P., Pant, M., Muda, A. (eds) Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-28031-8_18
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DOI: https://doi.org/10.1007/978-3-319-28031-8_18
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