In computer science, M-trees are tree data structures that are similar to R-trees and B-trees. It is constructed using a metric and relies on the triangle inequality for efficient range and k-nearest neighbor (k-NN) queries.

An M-Tree is an efficient access method for similarity search in metric spaces. It has better time complexity than other KNN algorithms. In practice, with a fast distance function, much simpler algorithms are faster.

An m-tree is a data structure which indexes objects according to their relative distances. It is efficient for nearest-neighbor queries. This implementation follows the content of the article http://www.vldb.org/conf/1997/P426.PDF, with the following highlights: * The data structure is the same described in the article.

Fur- trees, we can anyway see that M-tree isa competi- thermore, although M-tree hasan intrinsically wider applicability range, we consider important toevalu- tive access method even for indexing data from vector spaces.

An M-Tree (Metric Tree) is a data structure designed for organizing and searching large datasets in metric spaces. It enables efficient similarity search and nearest neighbor queries by leveraging the properties of metric spaces.

A metric tree is any tree data structure specialized to index data in metric spaces. Metric trees exploit properties of metric spaces such as the triangle inequality to make accesses to the data more efficient. Examples include the M-tree, vp-trees, cover trees, MVP trees, and BK-trees. [1]

Aug 9, 2001 · In this paper, we propose novel methods of continuous kNN join over dynamic high-dimensional data. We firstly propose the HDR+ Tree, which supports more efficient insertion, deletion, and batch...

M-tree is a dynamic tree. Fig. 1 shows an M-tree example. An intermediate (i.e., a non-leaf) entry e in a root node (e.g., N0) or a non-leaf node (e.g., N1, N2) records the following. (i) A...

The M-tree is an index structure that can be used for the efficient resolution of similarity queries on complex objects to be compared using an arbitrary metric, i.e. a distance function d that satisfies the positivity, symmetry, and triangle inequality postulates.

The M-tree is a dynamic data structure designed to index metric datasets. In this paper we introduce two dynamic techniques of building the M-tree. The first one incorporates a multi-way object insertion while the second one exploits the generalized slim-down algorithm.