Abstract
We present KD-DT, an algorithm that uses a decision-tree-inspired measure to build a kd-tree for low cost nearest-neighbor searches. The algorithm starts with a “standard” kd-tree and uses searches over a training set to evaluate and improve the structure of the kd-tree. In particular, the algorithm builds a tree that better insures that a query and its nearest neighbors will be in the same subtree(s), thus reducing the cost of subsequent search.
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© 1999 Springer-Verlag Berlin Heidelberg
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Talbert, D., Fisher, D. (1999). Exploiting Sample-Data Distributions to Reduce the Cost of Nearest-Neighbor Searches with Kd-Trees. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds) Advances in Intelligent Data Analysis. IDA 1999. Lecture Notes in Computer Science, vol 1642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48412-4_34
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DOI: https://doi.org/10.1007/3-540-48412-4_34
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