1 BST Trees 5 38 149. 2 A binary search tree is a binary tree in which every node satisfies the...
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Transcript of 1 BST Trees 5 38 149. 2 A binary search tree is a binary tree in which every node satisfies the...
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BST Trees
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A binary search tree is a binary tree in which every node satisfies the following:
• the key of every node in the left subtree is smaller than the key of this node• the key of every node in the right subtree is larger than the key of this node
Note: Duplication nodes ?
A binary search tree is a binary tree in which every node satisfies the following:
• the key of every node in the left subtree is smaller than the key of this node• the key of every node in the right subtree is larger than the key of this node
Note: Duplication nodes ?
Binary search tree (BST)
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Binary search tree
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The left subtree and the right subtree are also BST (Recursion)
No two nodes in a BST have the same key
If we traverse a BST inorder, we list the key in ascending order
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Some examples
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Is this a BST?
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This is NOT BST!
Every node satisfies the following: -- the key of the left node is smaller than the its own key -- the key of the right node is larger than its own key
Every node satisfies the following: -- the key of the left node is smaller than the its own key -- the key of the right node is larger than its own key
This is an incorrect check of BST.This is an incorrect check of BST.
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Search BST
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target
To find a key in a BST, we start at the root. Compare the target key with the key of the current node.
- target == p->key, Done.- target < p->key, go left- target > p->key, go right
To find a key in a BST, we start at the root. Compare the target key with the key of the current node.
- target == p->key, Done.- target < p->key, go left- target > p->key, go right
Note that the principle behind is similar to binary search...
Note that the principle behind is similar to binary search...
5Find
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Basic Functions
Search Find Min Find Max Insert(x) Delete
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C++ Implementation of BST Search // Internal method to find an item in a subtree.
// x is item to search for // t is the node that roots the tree // Return node containing the matched item.
template <class Comparable> BinaryNode<Comparable> * BinarySearchTree<Comparable>:: find( const Comparable & x, Node *t ) const { while( t != NULL ) { if( x < t->element ) // element is the key value for each node t = t->left; else if( t->element < x ) t = t->right; else return t; // Match } return NULL; // Not found }
// Internal method to find an item in a subtree. // x is item to search for // t is the node that roots the tree // Return node containing the matched item.
template <class Comparable> BinaryNode<Comparable> * BinarySearchTree<Comparable>:: find( const Comparable & x, Node *t ) const { while( t != NULL ) { if( x < t->element ) // element is the key value for each node t = t->left; else if( t->element < x ) t = t->right; else return t; // Match } return NULL; // Not found }
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Balance tree
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The efficiency of BST search depends on the shape of the tree. If the tree is balance, (minimum height, very bushy), the search is fast (log(n) steps). If the tree is a long narrow chain, the search is slow (n steps)
The efficiency of BST search depends on the shape of the tree. If the tree is balance, (minimum height, very bushy), the search is fast (log(n) steps). If the tree is a long narrow chain, the search is slow (n steps)
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log(16)=4
Compare this with sequential and binary search ...
Compare this with sequential and binary search ...
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Binary Search Tree Class - FindMin/Maxtemplate <class Comparable>
BinaryNode<Comparable> * BinarySearchTree<Comparable>::findMin( Node *t ) const{
if( t != NULL ){
while( t->left != NULL )
t = t->left; // the leftmost node
}
return t;
}
template <class Comparable>
BinaryNode<Comparable> * BinarySearchTree<Comparable>::findMax( Node *t ) const{
If( t != NULL )
while( t->right != NULL )
t = t->right; // the rightmost node
return t;
}
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Insert & Delete in BST First we’ll study simple-minded Insert
and Delete procedure. They may result in highly unbalanced tree
Then we’ll study a special kind of BST called AVL tree which maintains near balance in inserting and deleting.
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Insert in BST (recursive version)// Internal method to insert into a subtree
// x is the item to insert// t is the node that roots the tree// Set the new root// Throw an exception if x is already in ttemplate <class Comparable>void BinarySearchTree<Comparable>::insert( const Comparable & x, Node * & t ) const {
if( t == NULL )t = new Node( x, NULL, NULL );
else if( x < t->element )insert( x, t->left );
else if( x > t->element)insert( x, t->right );
elsethrow DuplicateItemException( );
}
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New nodes are always added as leaves.
New nodes are always added as leaves.
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Delete in BST
Case 1: If it has no children, that is, it is a leaf, we just remove it.
Case 1: If it has no children, that is, it is a leaf, we just remove it.
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Delete in BST
L
L
Case 2: If it has only one child, (i.e. only one subtree), we splice out it. That is, we attach that subtree to the parent of the node.
Case 2: If it has only one child, (i.e. only one subtree), we splice out it. That is, we attach that subtree to the parent of the node.
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Delete in BST, examples
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Case 2: delete a node that has only one subtree. Case 2: delete a node that has only one subtree.
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Delete in BST
Case 3: If it has nonempty left and right subtrees. • Find the minimum node in right subtree, then copy its key value to X, and remove the min node from right subtree.• Or Find the min node in right subtree, then swap it with the target node, and remove the target node
Case 3: If it has nonempty left and right subtrees. • Find the minimum node in right subtree, then copy its key value to X, and remove the min node from right subtree.• Or Find the min node in right subtree, then swap it with the target node, and remove the target node
RL
R’L
Min in R
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Delete Tree, example
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Case 3: delete a node that has nonempty left and right subtrees.
Case 3: delete a node that has nonempty left and right subtrees.
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Binary Search Tree Binary search tree, using simple insert and
delete procedures the tree is nearly balance
add - fast O(log n) delete a target - fast O(log n) search - fast O(log n)
the tree is highly unbalance, it becomes a singly linked list (the worst case) add, delete and search - slow O(n)
effectively using sequential search to find a location
