# Inorder Traversal

Tree traversal is a process of visiting each node in a tree data structure in a specific order. There are three common ways to traverse a tree: inorder, preorder, and postorder. In this article, we’ll focus on inorder traversal, which visits the left subtree, then the root node, and finally the right subtree.

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## What is Inorder Traversal?

If you’re new to tree data structures, the concept of inorder traversal might be a little confusing. Simply put, inorder traversal is the process of visiting each node in a tree in a specific order. The most common way to traverse a tree is called preorder traversal, which means that you visit the root node first, then the left subtree, and finally the right subtree. In contrast, postorder traversal visits the left subtree first, then the right subtree, and finally the root node.

Inorder traversal is somewhere in between these two extremes. When you perform inorder traversal on a tree, you first visit the left subtree, then the root node, and finally the right subtree. This order might seem arbitrary at first glance, but it turns out to be quite useful.

One common use case for inorder traversal is to print out all the values in a tree in sorted order. This is because if you visit the nodes of a tree in order from left to right, you will end up visiting them in sorted order if the tree is a binary search tree (BST).

## Applications of Inorder Traversal

One of the most common applications of inorder traversal is in the construction of binary search trees. By traversing the tree in inorder, we can construct the tree so that the leftmost element is always less than the current node and the rightmost element is always greater than the current node.

Inorder traversal can also be used to find the kth smallest element in a binary search tree. By keeping track of the number of elements visited during the traversal, we can stop when we reach the kth element and return its value.

Inorder traversal can also be used to construct a balanced binary search tree from a sorted array. This is done by recursively dividing the array into two halves, performing an inorder traversal on each half, and then combining the two halves into a single balanced tree.

## Working of Inorder Traversal

Inorder traversal is one of the most popular ways of traversing a tree data structure. It is a type of depth-first search. The idea behind inorder traversal is to visit the left child, then the root node, and finally the right child. This order is not always followed, but it is a good way to remember how inorder traversal works.

Inorder traversal can be used to solve many problems, such as finding the kth element in a tree or printing all the elements in a tree in sorted order.

To implement inorder traversal, we can use recursion or an iterative approach. The recursive approach is easier to understand and code, but the iterative approach is more efficient.

Recursive Approach

The recursive approach to inorder traversal is very simple. We just need to follow the below steps:

• Visit the left child of the root node.
• Visit the root node.
• Visit the right child of the root node.

Iterative Approach

The iterative approach to inorder traversal is a little bit more complex, but it is more efficient. The idea behind the iterative approach is to use stack data.

There are many advantages to using inorder traversal when working with trees.

• One of the biggest advantages is that it allows for easy retrieval of data. This is because, in inorder traversal, the leftmost child is always visited first. As a result, all of the data is stored in order, making it easy to retrieve later on.
• Another advantage of the inorder traversal is that it can be used to easily find out whether or not a tree is balanced. This is because the inorder traversal algorithm always visits the leftmost child first. As a result, if the tree is balanced, the algorithm will always visit the same number of nodes on each side of the tree.