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A vantage-point tree (or VP tree) is a metric tree that segregates data in a metric space by choosing a position in the space (the "vantage point") and partitioning the data points into two parts: those points that are nearer to the vantage point than a threshold, and those points that are not.
A vantage point tree (VP-Tree) is a data structure used for efficiently searching for nearest neighbors in metric spaces. It organizes data points in a tree structure based on their distances to a chosen vantage point. This enables faster search operations compared to traditional linear search methods.
Un VPT es un árbol binario en el que cada nodo representa un subconjunto S de individuos del conjunto inicial, utiliza un elemento especial del conjunto llamado pivote (vantage point) para dividir el conjunto S en dos subconjuntos, uno por cada hijo.
In this blog entry, I will half-heartedly describe some data structures for spatial search. Then I will launch into a detailed explanation of VP-Trees (Vantage Point Trees), which are simple, fast, and can easily handle low or high dimensional data.
Vantage Point Tree (or vp tree) is a space partitioning data structure that allows for efficient querying of nearest neighbors in high dimensional spaces.
Vantage-Point-trees (VPT) have been used in fewer applications than K-d trees and R-Trees, which have been in the areas such as image segmentation [14]. VPT are most advantageous for high dimensional data [26], where K-d trees and R-trees are known to degrade [22]. Recently, VPT have proven themselves useful in area of computer security [18, 2].
Vantage Point Trees. Based on “Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces”, Peter N. Yianilos (1993) - http://algorithmics.lsi.upc.edu/docs/practicas/p311-yianilos.pdf. (Image from https://fribbels.github.io/vptree/writeup) Nearest Neighbor Search.
