Binary Search Tree Improvements

What is one method to improve the efficiency of a binary search tree when faced with bad worst-case scenarios?

Which of the following tree structures allows more than one key per node?

Which of the following trees is specifically structured to maintain balance after insertions?

Which of the following tree types uses a representation-change to improve its efficiency?

Which imbalance condition does not directly trigger rebalancing in an AVL tree?

Overview of Binary Search Trees

Binary search trees can have poor performance in worst-case scenarios, displaying linear efficiency.
To improve efficiency, two main strategies involve rebalancing methods and allowing multiple keys per node.

Rebalancing Techniques

When a new insertion causes severe imbalance, rebalancing can help maintain efficiency.
- AVL Trees: Automatically maintain balance with height differences of no more than one between child nodes.
- Red-Black Trees: Use a coloring scheme to ensure balance, allowing a more flexible structure while maintaining efficiency.

Representation Change Techniques

Accepting multiple keys per node can ease the balance issues inherent in binary search trees.
- 2-3 Trees: Each node can have either two or three children, allowing for balanced growth.
- 2-3-4 Trees: An extension of 2-3 trees allowing up to four children, providing even more flexibility.
- B-Trees: A generalized form that can manage a large number of children per node, commonly used in database systems for efficient data retrieval.

Explore the various methods to improve the efficiency of binary search trees. This quiz covers techniques such as AVL trees, red-black trees, 2-3 trees, and B-trees. Test your understanding of how these structures enhance performance and maintain balance.