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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.
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.
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.
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.
Locality-Sensitive Hashing (LSH) is an algorithmic technique for approximate nearest neighbor search in high-dimensional vector spaces, commonly used in vector databases to speed up similarity search while reducing memory footprint.
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.
Optimized Product Quantization (OPQ) enhances Product Quantization by optimizing space decomposition and codebooks, leading to lower quantization distortion and higher accuracy in vector search. OPQ is widely used in advanced vector databases for improving recall and search quality.
Category: Concepts & Definitions
Tags: data-structure, metric-space, nearest-neighbor, dynamic-updates
M-tree (Metric Tree) is a dynamic index structure designed for organizing and searching large datasets in metric spaces. It enables efficient similarity search and nearest neighbor queries, which are essential for applications working with high-dimensional vectors, such as vector databases, multimedia databases, content-based image retrieval, and natural language processing tasks.
Read more about M-tree (Metric Tree)
No pricing information is applicable, as this is a data structure concept.