# data-structure

> Browse 5 items tagged with data-structure.

## Items

- [KD-Tree](https://vectordb.ever.works/tr/items/kd-tree) — 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.
- [Foundations of Multidimensional and Metric Data Structures](https://vectordb.ever.works/tr/items/foundations-of-multidimensional-and-metric-data-structures) — 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](https://vectordb.ever.works/tr/items/k-means-tree) — 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](https://vectordb.ever.works/tr/items/m-tree) — 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](https://vectordb.ever.works/tr/items/r-tree) — 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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