The Excellence
Directory Platform Template

Tree-based data structure for organizing vectors through recursive axis-aligned partitioning, enabling logarithmic time complexity searches for balanced data but struggling with high-dimensional spaces.

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.

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.

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.