What Is Tree in Data Structure Slideshare?


Larry Thompson

A tree is a widely used data structure in computer science and is an essential concept to understand for any aspiring programmer or developer. In this article, we will explore the concept of a tree in data structures and its significance.

What is a Tree?

In simple terms, a tree in data structure is a hierarchical structure that resembles a real-life tree. It consists of nodes connected by edges, where each node can have zero or more child nodes. The topmost node is called the root, and it has no parent.

Trees are often used to represent hierarchical relationships between elements or entities. For example, file systems on a computer can be represented as trees, with folders as nodes and files as leaf nodes.

Tree Terminology:

Before diving deeper into trees, let’s familiarize ourselves with some common terminology:

  • Node: Each element in a tree is called a node. It can have an arbitrary number of child nodes.
  • Root: The topmost node in the tree with no parent.
  • Parent: A node that has one or more child nodes.
  • Child: A node directly connected to another node when moving away from the root.
  • Sibling: Nodes that share the same parent are called siblings.
  • Leaf Node: Nodes with no children are called leaf nodes or terminal nodes.

The Importance of Trees

Trees are essential for organizing and representing hierarchical relationships efficiently. Here are some reasons why trees are widely used in computer science:

  • Efficient Searching: Trees provide efficient searching algorithms. Binary search trees, a type of tree data structure, allow for fast retrieval of data.
  • Sorting and Storing Data: Trees can be used to store and sort data in a structured manner. Binary heaps and AVL trees are examples of trees used for efficient sorting and storing.
  • Hierarchical Relationships: Trees are ideal for representing hierarchical structures like organization charts, family trees, or directory structures.
  • Decision-Making: Decision trees are widely used in artificial intelligence and machine learning algorithms to make decisions based on given conditions.

Types of Trees

Trees can have various types, depending on their characteristics and properties. Here are some common types of trees:

  • Binary Tree: A binary tree is a tree in which each node can have at most two children: left child and right child.
  • Binary Search Tree (BST): A binary search tree is a type of binary tree where the left child is always smaller than the parent node, and the right child is greater than or equal to the parent node. BSTs allow for efficient searching and sorting operations.
  • Balanced Tree: Balanced trees maintain a balance between the left and right subtrees, ensuring optimal performance for various operations.

    Examples include AVL trees, red-black trees, and B-trees.

  • N-ary Tree: An N-ary tree is a tree in which each node can have at most N children. It generalizes the concept of binary trees to accommodate more than two children per node.


Trees are versatile data structures that provide efficient ways to organize, search, and store data. Understanding the concept of trees and their types is crucial for designing efficient algorithms and solving complex problems. Whether you are building a file system, implementing a sorting algorithm, or working on artificial intelligence, trees will likely play a role in your programming journey.

So dive deeper into the world of trees and unlock the power of hierarchical relationships in your programs!

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