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Showing content from https://www.tutorialspoint.com/data_structures_algorithms/tree_data_structure.htm below:

Tree Data Structure

Tree Data Structure Tree Data Structrue

A tree is a non-linear abstract data type with a hierarchy-based structure. It consists of nodes (where the data is stored) that are connected via links. The tree data structure stems from a single node called a root node and has subtrees connected to the root.

Important Terms

Following are the important terms with respect to tree.

Types of Trees

There are three types of trees −

General Trees

General trees are unordered tree data structures where the root node has minimum 0 or maximum n subtrees.

The General trees have no constraint placed on their hierarchy. The root node thus acts like the superset of all the other subtrees.

Binary Trees

Binary Trees are general trees in which the root node can only hold up to maximum 2 subtrees: left subtree and right subtree. Based on the number of children, binary trees are divided into three types.

Full Binary Tree

Complete Binary Tree

Perfect Binary Tree

Binary Search Trees

Binary Search Trees possess all the properties of Binary Trees including some extra properties of their own, based on some constraints, making them more efficient than binary trees.

The data in the Binary Search Trees (BST) is always stored in such a way that the values in the left subtree are always less than the values in the root node and the values in the right subtree are always greater than the values in the root node, i.e. left subtree < root node right subtree.

Advantages of BST Disadvantages of BST

The main disadvantage of Binary Search Trees is that if all elements in nodes are either greater than or lesser than the root node, the tree becomes skewed. Simply put, the tree becomes slanted to one side completely.

This skewness will make the tree a linked list rather than a BST, since the worst case time complexity for searching operation becomes O(n).

To overcome this issue of skewness in the Binary Search Trees, the concept of Balanced Binary Search Trees was introduced.

Balanced Binary Search Trees

Consider a Binary Search Tree with m as the height of the left subtree and n as the height of the right subtree. If the value of (m-n) is equal to 0,1 or -1, the tree is said to be a Balanced Binary Search Tree.

The trees are designed in a way that they self-balance once the height difference exceeds 1. Binary Search Trees use rotations as self-balancing algorithms. There are four different types of rotations: Left Left, Right Right, Left Right, Right Left.

There are various types of self-balancing binary search trees −


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