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Ball Tree

A binary spatial tree that partitions a dataset into successively smaller and tighter ball nodes whose boundaries are defined by a hypersphere. Ball Tree works well in higher dimensions since the partitioning schema does not rely on a finite number of 1-dimensional axis aligned splits such as with k-d tree.

Interfaces: Binary Tree, Spatial

Data Type Compatibility: Depends on distance kernel

Parameters

#	Name	Default	Type	Description
1	maxLeafSize	30	int	The maximum number of samples that each leaf node can contain.
2	kernel	Euclidean	Distance	The distance kernel used to compute the distance between sample points.

Example

use Rubix\ML\Graph\Trees\BallTree;
use Rubix\ML\Kernels\Distance\Euclidean;

$tree = new BallTree(40, new Euclidean());

Additional Methods

This tree does not have any additional methods.

References

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1. S. M. Omohundro. (1989). Five Balltree Construction Algorithms. ↩

2. M. Dolatshah et al. (2015). Ball*-tree: Efficient spatial indexing for constrained nearest-neighbor search in metric spaces. ↩

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Last update: 2021-03-03