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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.
Vantage Point Tree (or vp tree) is a space partitioning data structure that allows for efficient querying of nearest neighbors in high dimensional spaces.
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.
A vantage point tree allows to do fast but exact nearest neighbor searches in any space provided that you have a distance function to measure the distance between any two points in that space. This implementation might need some tweaks in case it is used to index a very large number of points (especially the select_vp function in the code). About.
The proposed algorithm uses Vantage-Point-Trees to provide fast nearest neighbor search result-ing in optimal single-linkage clustering with time complexity O(Nlog2N). We offer adjustments to the algorithm that reduce the time complexity to O(NlogN), but we lose the guarantee of optimal clustering.
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.
