A is a data structure consisting of nodes, where each node has at most two children. Traversing a binary tree in different ways allows us to explore and search for specific elements. is a technique where we visit the current node first, followed by recursively traversing the left subtree and then the right subtree. also follows a depth-first search approach but visits the left subtree, then the current node, and finally the right subtree. traverses the left subtree and the right subtree before visiting the current node. These traversal methods are useful for processing the nodes of a binary tree in various orders, depending on our needs.
Depth-first search and are two common algorithms for searching elements in a binary tree. Depth-first search explores as far as possible in the depth of a branch before backtracking. It can be implemented using any of the three traversal methods mentioned earlier. On the other hand, breadth-first search explores the tree layer by layer, visiting all nodes at the current depth level before moving on to the next level. This technique ensures that elements at a lower depth are always visited before elements at a higher depth.
A special case of a binary tree is a (BST), which follows specific ordering rules. In a BST, the left child of a node contains a value less than or equal to the node value, while the right child contains a value greater than the node value. This ordering property allows for efficient searching and insertion operations. By traversing a BST using any of the traversal methods, we can efficiently access the elements in sorted order.
