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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.
NMSLIB is an efficient similarity search library and toolkit for high-dimensional vector spaces, supporting a variety of indexing algorithms for vector database use cases.
Vexvault is an open-source vector database designed for efficient storage, management, and similarity search of high-dimensional vector data.
OasysDB is an open-source vector database focused on efficient similarity search and management of high-dimensional data.
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.
Category: Concepts & Definitions
Tags: clustering, data-structure, similarity-search, high-dimensional
K-means Tree is a clustering-based data structure designed to organize high-dimensional vectors for efficient similarity search and retrieval. It is commonly used as an indexing method in vector databases to optimize the performance of vector search operations.
Note: No pricing information is provided, as this is a concept/data structure rather than a commercial product or service.