Analysis and Comparison of Various Sorting Algorithms

Abstract

Sorting algorithms are crucial in various aspects due to their significant role one of the most commonly used techniques. As we deal with data and handle large datasets on a day-to-day basis for various purposes such as analysis, comparison display (in ascending or descending order), and data processing, the need arises for data to be sorted efficiently. This allows for the fast and efficient retrieval of useful information from large collections of data. Sorting large datasets enhances data accessibility, improves system performance, and enables better decision-making processes. Sorting algorithms play a vital role in many areas like data mining and managing databases and information retrieval. In this study, we provide the core advantages and disadvantages of each modern sorting algorithm, such as Bubble Sort, Insertion Sort, Selection Sort, Quick Sort, Merge Sort, and Heap Sort, which are frequently used to sort large datasets in order to retrieve information efficiently and quickly. Through this analysis, we aim to identify the most appropriate algorithm for a given scenario. Each technique's specific functionality, importance, advantages, and disadvantages have been discussed in detail. We also provide the approximate time taken by each algorithm, known as time complexity, to provide a better understanding of their performance. Additionally, we demonstrate the practical implementation of different algorithms and calculate the time taken by each to display data in sorted order. Sorting algorithms facilitate faster search operations, enable easier data analysis, and support more effective data processing workflows. Overall, the ability of sorting algorithms to handle large datasets efficiently is essential for optimizing data management and analysis tasks in modern computing environments.