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English: Analysis of data structures, tree compared to hash and array based structures, height balanced tree compared to more perfectly balanced trees, a simple height balanced tree class with test code, comparable statistics for tree performance, statistics of worst case strictly-AVL-balanced trees versus perfect full binary trees.
In a binary tree the balance factor of a node X is defined to be the height difference ():= (()) (()) [6]: 459 of its two child sub-trees rooted by node X. A node X with () < is called "left-heavy", one with () > is called "right-heavy", and one with () = is sometimes simply called "balanced".
Various height-balanced binary search trees were introduced to confine the tree height, such as AVL trees, Treaps, and red–black trees. [5] The AVL tree was invented by Georgy Adelson-Velsky and Evgenii Landis in 1962 for the efficient organization of information. [6] [7] It was the first self-balancing binary search tree to be invented. [8]
For height-balanced binary trees, the height is defined to be logarithmic () in the number of items. This is the case for many binary search trees, such as AVL trees and red–black trees . Splay trees and treaps are self-balancing but not height-balanced, as their height is not guaranteed to be logarithmic in the number of items.
BATON is considered balanced if and only if the height of its two sub-trees at any node in the tree differs by at most one. If any node detects that the height-balanced constraint is violated, a restructuring process is initiated to ensure that the tree remains balanced.
If the two trees are balanced, join simply creates a new node with left subtree t 1, root k and right subtree t 2. Suppose that t 1 is heavier (this "heavier" depends on the balancing scheme) than t 2 (the other case is symmetric). Join follows the right spine of t 1 until a node c which is balanced with t 2.
Let h ≥ –1 be the height of the classic B-tree (see Tree (data structure) § Terminology for the tree height definition). Let n ≥ 0 be the number of entries in the tree. Let m be the maximum number of children a node can have. Each node can have at most m−1 keys.
An AA tree in computer science is a form of balanced tree used for storing and retrieving ordered data efficiently. AA trees are named after their originator, Swedish computer scientist Arne Andersson. [1] AA trees are a variation of the red–black tree, a form of binary search tree which supports efficient addition and deletion of entries ...