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Does binary tree and binary search tree same?
no they are not same
How can you merge two binary search trees into a single binary search tree?
To merge two binary search trees into a single binary search tree, you can perform an in-order traversal on each tree to extract their elements, combine the elements into a single sorted list, and then construct a new binary search tree from the sorted list. This process ensures that the resulting tree maintains the binary search tree property.
What is complexity of binary search tree?
The complexity of binary search tree : Search , Insertion and Deletion is O(h) . and the Height can be of O(n) ( if the tree is a skew tree). For Balanced Binary Trees , the Order is O(log n).
How do you print all data in a Binary Search Tree?
By using Depth First Search or Breadth First search Tree traversal algorithm we can print data in Binary search tree.
What is a difference between binary tree and binary?
I think a binary tree is a thing to help you search whereas binary is 100100101010, that thing that computers use...I think the difference is that a binary tree helps you search but binary is the thing that computers use:10010101001010 The term binary refers to the idea that there are "2" options. In terms of computers at a low level, this refers to 1's and 0's (high voltage and low voltage). A binary tree is a completely different concept. It is a type of data structure with a parent node that branches down into 2 child nodes at each level. If implemented as a binary *search* tree it is pretty efficient at searching data sets that are ordered (O(log n))
Difference between B-tree and Binomial search tree?
A B-tree is a kind of tree data structure which is a generalization of a binary search tree where each node can have more than two children and contain more than 1 value. A Binominal search tree I am not sure of. If you mean Binary search tree, then it is an abstract data structure. Binominal is a term usually used with distributions while Binary is usually used with data. Hope this helps.
Is an AVL tree a binary search tree (BST)?
Yes, an AVL tree is a type of binary search tree (BST) that is balanced to ensure efficient searching and insertion operations.
What are the key differences between a heap and a binary search tree (BST)?
A heap is a complete binary tree where each node has a value greater than or equal to its children (max heap) or less than or equal to its children (min heap). A binary search tree is a binary tree where the left child of a node has a value less than the node and the right child has a value greater than the node. The key difference is that a heap does not have a specific order between parent and child nodes, while a binary search tree maintains a specific order for efficient searching.
Are binary search trees always balanced?
No, binary search trees are not always balanced. Balancing a binary search tree involves ensuring that the height difference between the left and right subtrees of each node is at most 1. Unbalanced binary search trees can lead to inefficient search and insertion operations.
Is sorting a binary search tree simple?
A binary search tree is already ordered. An in order traversal will give you a sorted list of nodes.
What are the key differences between an AVL tree and a binary search tree, and how do these differences impact their performance and efficiency in terms of search operations?
An AVL tree is a self-balancing binary search tree where the heights of the two child subtrees of any node differ by at most one. This ensures that the tree remains balanced, leading to faster search operations. In contrast, a binary search tree does not have this balancing property, which can result in an unbalanced tree and slower search times. Overall, AVL trees are more efficient for search operations due to their balanced nature, while binary search trees may require additional operations to maintain balance and optimize performance.
What is the time complexity of searching a binary search tree?
The time complexity of searching a binary search tree is O(log n), where n is the number of nodes in the tree.
