This project was undertaken as part of our Data Structures course. The primary objective of this project is to implement and analyze Red-Black Trees (RBT) and Binary Search Trees (BST). The project focuses on the comparative performance, efficiency, and use cases of these two fundamental data structures.
The implementation includes a detailed exploration of the insertion, deletion, and search operations, along with performance benchmarks to highlight the advantages and disadvantages of each structure. Additionally, the project provides visualizations to aid in understanding the behavior of RBT and BST.
- Implement Red-Black Trees and Binary Search Trees in Python.
- Analyze and compare the performance of RBT and BST in various scenarios.
- Provide visualizations to demonstrate the structure and behavior of the trees.
- Discuss the practical applications and theoretical underpinnings of these data structures.
- Data Structure Implementation
- Performance Benchmarking
- Visualization of Tree Operations
- Comparative Analysis
The analysis revealed that Red-Black Trees offer better performance in terms of balancing compared to Binary Search Trees, which can degrade to linear time in worst-case scenarios. The visualizations and benchmarks provide a clear comparison, illustrating the efficiency of RBTs in maintaining balanced trees.