R-tree is a tree data structure widely used for indexing multi-dimensional information such as vectors, supporting efficient spatial queries like nearest neighbor and range queries, which are essential in vector databases.
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Ball-tree is a binary tree data structure used for organizing points in a multi-dimensional space, particularly useful in vector databases for nearest neighbor search. It partitions data points into hyperspheres (balls), enabling efficient search and scalability in high-dimensional vector spaces.
M-tree is a dynamic index structure for organizing and searching large data sets in metric spaces, enabling efficient nearest neighbor queries and dynamic updates, which are important features for vector databases handling high-dimensional vectors.
IVF is an indexing technique widely used in vector databases where vectors are clustered into inverted lists (partitions), enabling efficient Approximate Nearest Neighbor search by probing only a subset of relevant partitions at query time.
Technical book covering theory and practice of multidimensional and metric data structures for similarity search, forming a theoretical basis for index structures used in vector databases.
K-means Tree is a clustering-based data structure that organizes high-dimensional vectors for fast similarity search and retrieval. It is used as an indexing method in some vector databases to optimize performance for vector search operations.
Online Product Quantization (O-PQ) is a variant of product quantization designed to support dynamic or streaming data. It enables adaptive updating of quantization codebooks and codes in real-time, making it suitable for vector databases that handle evolving datasets.
Category: Concepts & Definitions
Tags: data-structure, spatial-indexing, vector-search, nearest-neighbor
R-tree is a balanced tree data structure designed for indexing multi-dimensional information such as geographical coordinates, rectangles, or polygons. It supports efficient spatial queries like nearest neighbor and range searches, making it widely used in spatial databases and vector search applications.