# What Is Heap Sort in Data Structure?

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Angela Bailey

What Is Heap Sort in Data Structure?

In the world of data structures and algorithms, there are various sorting algorithms that come into play when it comes to efficiently organizing and arranging data. One such algorithm is Heap Sort. Heap Sort is a comparison-based sorting algorithm that works by creating a binary heap data structure.

## Binary Heap

A binary heap is a complete binary tree in which every parent node satisfies the heap property. The heap property states that for every node i, the value of its parent node is greater than or equal to the value of i. This property ensures that the largest element in the heap is always at the root.

## How Does Heap Sort Work?

The process of Heap Sort involves two main steps: building a heap and then repeatedly removing the largest element from it.

### Step 1: Building a Heap

To build a heap, we start with an unsorted array of elements. We can visualize this array as a complete binary tree. We begin from the last non-leaf node and move upwards, ensuring that each subtree rooted at node i satisfies the heap property.

To satisfy the heap property, if any child node has a greater value than its parent node, we swap them to maintain the order. This process continues until we reach the root node.

### Step 2: Removing Elements from the Heap

Once we have built our heap with all elements satisfying the heap property, we can remove elements one by one to get them in sorted order. The removed elements are placed at the end of the array or another data structure as per requirement.

To remove an element, we swap it with the last element in the heap and then adjust the heap to satisfy the heap property again. This process is repeated until all elements have been removed from the heap, resulting in a sorted array.

## Time Complexity

The time complexity of Heap Sort is O(n log n), where n represents the number of elements in the array. This makes Heap Sort efficient for large datasets.

• Heap Sort guarantees sorting in-place, meaning it does not require additional memory beyond the input array.
• It has a predictable time complexity and performs well for large datasets.