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.Keywords k-nearest neighbors ⋅ k-NN ⋅ Hexagon ⋅ Hexagonal grid ⋅ Hexagonal coordinate system ⋅ Point cloud
Related WorkThe k-NN problem solves the searching of the k closest points from a query point in an unordered point cloud (PC). A condition of a maximum radius of the neighborhood can be added. The k-NN is an important part of many algorithms like clustering, classification and generally machine learning algorithms. It can be applied
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