Sorting is a process of arranging entities in a particular order, i.e. ascending, descending, or alphabetical order. Data structure sorting concerns the arrangement of data. If your domain is Information Technology or Computer Science, then you might have come across terms like Quick Sort, Bubble Sort, Merge Sort, and so on.
These are different sorting algorithms that depend on various factors like data structure, complexity, etc. One of the popular sorting algorithms that we are going to discuss here is the Heap Sort. It is very similar to Selection Sort, where the maximum value is selected and placed at the end of the list or array. Let us dig in to understand this better.
In Heap Sorting, as the name suggests, the first step is the process of creating heaps, or clustering in general terms. We create a Max Heap to sort the elements in ascending order. Once the heap is created, we swap the root note with the last node and delete the previous node from the heap.
Time and Space Complexity of Heap Sorting in Data Structure
- Best = Ω(n log(n))
- Average = Θ(n log(n))
- Worst = O(n log(n))
- The space complexity of Heap Sort is O(1).
Similarly, there is a concept of Max Heap and Min Heap. Like trees and arrays, there is another organized Data Structure called Heap Data Structure. It is a complete binary tree that follows the rule that all the root nodes are larger(for Max Heap) or smaller (for Min Heap) than their child nodes. This process is called Heapify. The image given below is a self-explanatory diagram of Min and Max Heaps.
Also Read: Sorting in Data Structure
Advantages and Disadvantages of using Heap Sort in Data Structure
Advantages: Optimized performance, efficiency, and accuracy are a few of the best qualities of this algorithm. The algorithm is also highly consistent with very low memory usage. No extra memory space is required to work, unlike the Merge Sort or recursive Quick Sort.
Disadvantages: Heap Sort is considered unstable, expensive, and not very efficient when working with highly complex data.
Applications of Heap Sorting
You might have come across Dijkstra’s algorithm that finds the shortest path where Heap Sort is implemented. Heap Sort in Data Structure is used when the smallest (shortest) or highest (longest) value is needed instantly. Other usages include finding the order in statistics, dealing with priority queues in Prim’s algorithm (also called the minimum spanning tree) and Huffman encoding or data compression.
Similarly, various operating systems use this algorithm for jobs and process management as it’s based on the priority queue.
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Taking an example from real life —Heap Sorting can be applied to a sim card store where there are many customers in line. The customers who have to pay bills can be dealt with first because their work will take less time. This method will save time for many customers in line and avoid unnecessary waiting.
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To every type of sorting or searching algorithm, advantages and disadvantages are always there. With Heap Sorting algorithms, there are very few disadvantages. There is no additional requirement of memory space.
The other factor is time. It is found that the time complexity is calculated as nlog(n), but the actual Heap Sort is lesser than O(nlog(n)). The reason is that reduction or extraction from the Heap Sort reduces the size and takes much less time as the process goes on.
Hence, Heap Sorting is considered one of the ”best” sorting algorithms because of various reasons in the world of Data Structure.
Data structure and its organizations are one of the fundamentals of computer science. If the logical and practical knowledge of the individual is strong, then they can ace in fields like programming. It’s not just about excelling in the course, but one can not move ahead in programming without the knowledge of Data Structure.
So the next step that you have to take is to get yourself registered for the course you want. I would suggest UpGrad’s Lifelong Learning initiative which will not only cover some basic topics like Heap Sort in Data Structure but also give you knowledge about Data Science, Technology Management, and Digital Marketing.
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