A Binary Search Tree (BST) is a data structure used to storing data in a sorted manner. Each node in a Binary Search Tree has at most two children, a left child and a right child, with the left child containing values less than the parent node and the right child containing values greater than the parent node. This hierarchical structure allows for efficient searching, insertion, and deletion operations on the data stored in the tree.
Binary Search Tree
Applications of Binary Search Tree (BST):
- Searching: Finding a specific element in a sorted collection
- Sorting: Sorting a collection of elements in ascending or descending order
- Range queries: Finding elements within a specified range
- Data storage: Storing and retrieving data in a hierarchical manner
- Databases: Indexing data for efficient retrieval
- Computer graphics: Representing spatial data in a tree structure
- Artificial intelligence: Decision trees and rule-based systems
Advantages of Binary Search Tree (BST):
- Efficient searching: O(log n) time complexity for searching
- Ordered structure: Elements are stored in sorted order, making it easy to find the next or previous element
- Dynamic insertion and deletion: Elements can be added or removed efficiently
- Balanced structure: Balanced BSTs maintain a logarithmic height, ensuring efficient operations
- Space efficiency: BSTs store only the key values, making them space-efficient
Disadvantages of Binary Search Tree (BST):
- Not self-balancing: Unbalanced BSTs can lead to poor performance
- Worst-case time complexity: In the worst case, BSTs can have a linear time complexity for searching and insertion
- Memory overhead: BSTs require additional memory to store pointers to child nodes
- Not suitable for large datasets: BSTs can become inefficient for very large datasets
- Limited functionality: BSTs only support searching, insertion, and deletion operations