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Data Structure Tutorial

Overview

Arrays in Data Structures: A Guide With Examples

Lesson - 1

All You Need to Know About Two-Dimensional Arrays

Lesson - 2

All You Need to Know About a Linked List in a Data Structure

Lesson - 3

The Complete Guide to Implement a Singly Linked List

Lesson - 4

The Ultimate Guide to Implement a Doubly Linked List

Lesson - 5

The Fundamentals for Understanding Circular Linked List

Lesson - 6

The Ultimate Guide To Understand The Differences Between Stack And Queue

Lesson - 7

Implementing Stacks in Data Structures

Lesson - 8

Your One-Stop Solution for Stack Implementation Using Array

Lesson - 9

Your One-Stop Solution for Queue Implementation Using Array

Lesson - 10

Your One-Stop Solution to Learn Depth-First Search(DFS) Algorithm From Scratch

Lesson - 11

Your One-Stop Solution for Stack Implementation Using Linked-List

Lesson - 12

The Definitive Guide to Understand Stack vs Heap Memory Allocation

Lesson - 13

All You Need to Know About Linear Search Algorithm

Lesson - 14

All You Need to Know About Breadth-First Search Algorithm

Lesson - 15

A One-Stop Solution for Using Binary Search Trees in Data Structure

Lesson - 16

The Best Tutorial to Understand Trees in Data Structure

Lesson - 17

A Complete Guide to Implement Binary Tree in Data Structure

Lesson - 18

A Holistic Look at Using AVL Trees in Data Structures

Lesson - 19

All You Need to Know About Tree Traversal in Data Structure

Lesson - 20

The Best and Easiest Way to Understand an Algorithm

Lesson - 21

The Best Guide You’ll Ever Need to Understand B-Tree in Data Structure

Lesson - 22

The Best Guide You'll Ever Need to Understand Spanning Tree in Data Structure

Lesson - 23

A One-Stop Solution Guide to Understand Data Structure and Algorithm Complexity

Lesson - 24

Your One-Stop Solution to Understand Shell Sort Algorithm

Lesson - 25

Your One-Stop Solution to Quick Sort Algorithm

Lesson - 26

What Is Selection Sort Algorithm In Data Structures?

Lesson - 27

Everything You Need to Know About Radix Sort Algorithm

Lesson - 28

Everything You Need to Know About the Counting Sort Algorithm

Lesson - 29

Everything You Need to Know About the Merge Sort Algorithm

Lesson - 30

Insertion Sort Algorithm: One-Stop Solution That Will Help You Understand Insertion Sort

Lesson - 31

Everything You Need to Know About the Bubble Sort Algorithm

Lesson - 32

The Best Guide You’ll Ever Need to Understand Bucket Sort Algorithm

Lesson - 33

Your One-Stop Solution to Understand Recursive Algorithm in Programming

Lesson - 34

The Definitive Guide to Understanding Greedy Algorithm

Lesson - 35

Your One-Stop Solution to Understand Backtracking Algorithm

Lesson - 36

The Fundamentals of the Bellman-Ford Algorithm

Lesson - 37

Your One-Stop Solution for Graphs in Data Structures

Lesson - 38

The Best Guide to Understand and Implement Solutions for Tower of Hanoi Puzzle

Lesson - 39

A Simplified and Complete Guide to Learn Space and Time Complexity

Lesson - 40

All You Need to Know About the Knapsack Problem : Your Complete Guide

Lesson - 41

The Fibonacci Series: Mathematical and Programming Interpretation

Lesson - 42

The Holistic Look at Longest Common Subsequence Problem

Lesson - 43

The Best Article to Understand What Is Dynamic Programming

Lesson - 44

A Guide to Implement Longest Increasing Subsequence Using Dynamic Programming

Lesson - 45

A Holistic Guide to Learn Stop Solution Using Dynamic Programming

Lesson - 46

One Stop Solution to All the Dynamic Programming Problems

Lesson - 47

Understanding the Fundamentals of Binomial Distribution

Lesson - 48

Here’s All You Need to Know About Minimum Spanning Tree in Data Structures

Lesson - 49

Understanding the Difference Between Array and Linked List

Lesson - 50

The Best Article Out There to Understand the B+ Tree in Data Structure

Lesson - 51

A Comprehensive Look at Queue in Data Structure

Lesson - 52

Your One-Stop Solution to Understand Coin Change Problem

Lesson - 53

The Best Way to Understand the Matrix Chain Multiplication Problem

Lesson - 54

Your One-Stop Solution to Learn Floyd-Warshall Algorithm for Using Dynamic Programming

Lesson - 55

The Best Tutorial You'll Ever Need for Queue Implementation Using Linked List

Lesson - 56

All You Need to Know About How to Create a React JS Portfolio Project

Lesson - 57

The Best Guide to Learn How to Make a Wireframe: What is a Wireframe?

Lesson - 58

The Best DSA Projects for Your Resume

Lesson - 59

Top 5 Best Coding Books You Must Read

Lesson - 60

The Working and Implementation of Physical Layer in the OSI Model

Lesson - 61

How to Create an Instagram Clone Using React?

Lesson - 62

ReactJS Vs VueJS

Lesson - 63

Tree Traversal In Data Structure: Overview, Types, and More

Lesson 20 of 63By Simplilearn

Last updated on Feb 28, 202327553
All You Need to Know About Tree Traversal in Data Structure

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A tree is a hierarchical data structure made up of nodes and connected by the edges. HTML and XML are two markup languages. Also, you can learn what Hyper Text Markup Language is and how it can be used. Both HTML and XML use a tree structure, with a root that includes child branches, which in turn may have their own child branches, and so on. Tree traversal in a data structure is a type of graph traversal in computer science. Interested in learning more about data structures? Here you will find information about data structures, their types, applications and many more.

What is Tree Traversal in Data Structure?

Tree traversal in a data structure is a type of graph traversal in the data structure that refers to the process of visiting, verifying, and updating each node in a tree data structure just once. The order in which you examine the nodes of the tree is used to classify these traversals. 

Tree-traversal-in-data-structure.

Next, you will see some data structures which are used in the tree traversal.

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Data Structure Used for Implementing Tree Traversal

Iterating over all nodes is required when traversing a tree data structure. Because there is more than one possible next node from a given node because a tree is not a linear data structure, it must postpone specific nodes must and store them in some fashion for later visits, assuming sequential execution. 

In a tree data structure, there are primarily two data structures that are used for traversal:

  • Stack Data Structure

This first data structure is a stack, which is a container for added and removed items according to the last-in-first-out (LIFO) principle.

  • Queue Data Structure

Now let's look at the basic operations and queue in data structure and discover what they all mean. The first-in, first-out (FIFO) concept governs how you add and withdraw objects in linear data sets from a queue.

You will cover the varieties of tree traversal in the data structure next in this data structure tutorial.

Types of Tree Traversal in Data Structure

You can perform tree traversal in the data structure in numerous ways, unlike arrays, linked lists, and other linear data structures, which are canonically traversed in linear order. You can walk through them in either a depth-first search or a breadth-first search. In-order, pre-order, and post-order are the three most frequent ways to go over them in depth-first.

Beyond these fundamental traversals, more complex or hybrid techniques, such as depth-limited searches, such as iterative deepening depth-first search, are feasible.

Depth First Search 

The depth-first search (DFS) is a method of traversing or searching data structures composed of trees or graphs.

To get to the recursion, go down one level. If the tree isn't empty, perform the three procedures below in a specific order:

Left: Recursively traversed left subtree

Right: Recursively traversed right subtree

Node: Traverse the root node

Three orders execute tree traversal under this depth-first search algorithm.

Pre-Order Traversal ( Root-Left-Right)

  1. Traverse root node
  2. Traverse left subtree
  3. Traverse right subtree

preorder-tree-traversal-in-data-structure

Uses of pre-order traversal

To duplicate the tree, you need to use pre-order traversal. Pre-order traversal is used to obtain a prefix expression from an expression tree.

In-Order Traversal ( Left-Root-Right)

  1. Traverse left subtree
  2. Traverse root node
  3. Traverse right subtree

TREE-TRAVERSAL-IN-DATA-STRUCTURE.

Uses of in-order traversal

Inorder traversal gives nodes in non-decreasing order in binary search trees or BST. A version of Inorder traversal with Inorder traversal reverse can access BST nodes in non-increasing order.

Post-Order Traversal ( Left-Right-Root)

  1. Traverse left subtree
  2. Tereveser right subtree
  3. Traverse root node

postorder-traversal-in-data-structure

Uses of in-order traversal

You can delete the tree using post-order traversal. The postfix expression of tree data structure can also be obtained using post-order traversal.

Depth First Search or Level Order Traversal

BFS (Breadth-First Search) is a vertex-based method for determining the shortest path in a graph. It employs a queue data structure, where first in, it follows first out. In BFS, it visits one vertex and marks it at the time, after which it sees and queues its neighbors.

After understanding the types of tree traversal in data structures, you will now examine how to implement tree traversal in data structures.

LEVEL-ORDER-TRAVERSAL-IN-DATA-STRUCTURE

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Implementations of Tree Traversal in Data Structure

Pre-Order Traversal 

preorder_traversal(node)

    if (node == null)

        return

    visit(node)

    preorder_traversal(node.left_subtree)

    preorder_traversal(node.right_subtree)

In-Order Traversal 

inorder_traversal(node)

    if (node == null)

        return

    inorder_traversal(node.left_subtree)

    visit(node)

    inorder_traversal(node.right_subtree)

In-Order Traversal 

postorder_traversal(node)

    if (node == null)

        return

    postorder_traversal(node.left_subtree)

    postorder_traversal(node.right_subtree)

    visit(node)

Code of implementing all three tree traversal in data structure

#include <stdio.h>

#include<conio.h>

#include <stdlib.h>

enum Traversal {Preorder_Traversal, Inorder_Traversal, Postorder_Traversal};

typedef enum Traversal trav;

typedef struct Node Node;

struct Node {

    int x;

    Node* left_node, *right_node;

};

Node* create_tree(int data) {                            //creating tree

    Node* root = (Node*) malloc (sizeof(Node));

    root->left_node= root->right_node = NULL;

    root->x = data;

    return root;

}

Node* create_node(int data) {                          //creating node

    Node* node = (Node*) malloc (sizeof(Node));

    node->x = data;

    node->left_node = node->right_node = NULL;

    return node;

}

void release_tree(Node* root) {                        //releasing tree

    Node* t = root;

    if (!t)

        return;

    release_tree(t->left_node);

    release_tree(t->right_node);

    if (!t->left_node && !t->right_node) {

        free(t);

        return;

    }

}

 void displaytree(Traversal trav, Node* root) {

    if (!root)

        return;

    switch(trav) {

        case (Preorder_Traversal):      //preorder traversal

            printf("%d -> ", root->x);

            displaytree(trav, root->left_node);

            displaytree(trav, root->right_node);

            break;

        case (Inorder_Traversal):      //inorder traversal

            displaytree(trav, root->left_node);

            printf("%d -> ", root->x);

            displaytree(trav, root->right_node);

            break;

        case (Postorder_Traversal):   // preorder traversal

            displaytree(trav, root->left_node);

            displaytree(trav, root->right_node);

            printf("%d -> ", root->x);

            break;

    }

}

int main() {

    Node* root = create_tree(5);    

    root->left_node = create_node(10);

    root->right_node = create_node(15);

    root->left_node->left_node = create_node(20);

    root->left_node->right_node = create_node(25);

    root->right_node->left_node = create_node(30);

    root->right_node->right_node = create_node(35); 

    printf("Preorder Traversing\n");

    displaytree(Preorder_Traversal, root);

    printf("\n\n");

    printf("Inorder Traversing\n");

    displaytree(Inorder_Traversal, root);

    printf("\n\n");

    printf("Postorder Traversing\n");

    displaytree(Postorder_Traversal, root);

    release_tree(root);

    return 0;

}

Output of tree traversal code:

Preorder Traversing

5 -> 10 -> 20 -> 25 -> 15 -> 30 -> 35 ->

Inorder Traversing

20 -> 10 -> 25 -> 5 -> 30 -> 15 -> 35 ->

Postorder Traversing

20 -> 25 -> 10 -> 30 -> 35 -> 15 -> 5 ->

Finally, in this tutorial, you will look at various tree traversal applications in data structures.

Applications of Tree Traversal in Data Structure

  • To build a prefix expression (Polish notation) from expression trees, utilize pre-order traversal: traverse the expression tree in a pre-orderly manner.
  • For e.g. A+B * ( C-D) -> A +B * -CD -> +AB * -CD -> *+AB-CD

A prefix notation is known as Polish notation. All operators must come before the two operands they work on in prefix expression notation.

  • You can generate a binary tree's postfix representation or reverse Polish notation using post-order traversal.
  • For e.g. A+B * ( C-D) -> A +B * CD- -> AB+ * CD- -> AB+CD-*

The operators must occur after the corresponding operands in postfix notation, also known as reverse polish notation.

  • Because it returns values from the underlying set in order, according to the comparator setting up the binary search tree, it often employs traversal on binary search trees.

You can delete or release a complete binary tree when deleting or freeing nodes and values in the post-order traversal. As a result, it liberates the node once it frees its children.

Next Step

You learned how to do tree traversal in data structures and which data structures you require to implement tree traversal in this lesson. And you also learned about different forms of tree traversal in data structures.

"Graphs in Data Structures" will be your next topic, where you will learn what a graph data structure is and the types of graph data structure. If you want to learn more about data structures and programming languages, go to Simplilearn's Post Graduate Program In Full Stack Web Development course and start learning.

Please feel free to ask any questions you may have about the "tree traversal in data structure" tutorial in the comments section below. We will gladly assist you in resolving your issues as quickly as possible. 

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