Binary search requires that the list be in search key order.
A binary search tree is already ordered. An in order traversal will give you a sorted list of nodes.
A tree doesn't do anything so it has no speed...
* search array => O(1) linked list=> O(n) binary tree=> O(log n) hash=>O(1) * search array => O(1) linked list=> O(n) binary tree=> O(log n) hash=>O(1)
the major limitation of binary search is that there is a need of sorted array to perform binary search operation. if array is not sorted the output is either not correct or may be after a long number of steps and according to data structure the output should come in minimum number of steps.
Average case complexity for Binary search O(log N). (Big O log n)Habibur Rahman (https://www.facebook.com/mmhabib89)BUBT University Bangladeshhttp://www.bubt.edu.bd/
The only drawback I know of is that binary search requires that the list already be sorted. So if you have a really large unsorted list than binary search would not be the best option.
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.
No.
4 more info search how dangerous is the swine flu
A binary search tree is already ordered. An in order traversal will give you a sorted list of nodes.
It's called "Linear Search". If the list is sorted, then it is possible to perform more advanced searches like binary search. If the list isn't sorted, then you can either sort the list first and then binary search or simply use a linear search. Linear search is typically a brute force solution when the data isn't "planned" or if the data is stored in a linked list where random access of the values in the list is slow.
The best case for a binary search is finding the target item on the first look into the data structure, so O(1). The worst case for a binary search is searching for an item which is not in the data. In this case, each time the algorithm did not find the target, it would eliminate half the list to search through, so O(log n).
A tree doesn't do anything so it has no speed...
* search array => O(1) linked list=> O(n) binary tree=> O(log n) hash=>O(1) * search array => O(1) linked list=> O(n) binary tree=> O(log n) hash=>O(1)
the major limitation of binary search is that there is a need of sorted array to perform binary search operation. if array is not sorted the output is either not correct or may be after a long number of steps and according to data structure the output should come in minimum number of steps.
Average case complexity for Binary search O(log N). (Big O log n)Habibur Rahman (https://www.facebook.com/mmhabib89)BUBT University Bangladeshhttp://www.bubt.edu.bd/
There is no such thing. There are binary trees and linked lists.