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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.
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. 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.
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].
For the first part, we designed a bi-directional construction algorithm for Metric Tree (M-tree). It increased the construction speed and improved the query performance. Then, we conducted a comparative analysis of the performance of M-tree, General Hyper-plane Tree (GHT) and Vantage Point Tree (VPT) on biological work loads, and VPT turned out to be the optimal choice.
