1. 1 Trees Linear access time of linked lists is prohibitive –Does there exist any simple data structure for which the running time of most operations (search, insert, delete) is O(log N)?

2. 2 Trees A tree is a collection of nodes –The collection can be empty –(recursive definition) If not empty, a tree consists of a distinguished node r (the root), and zero or more nonempty subtrees T 1, T 2,...., T k, each of whose roots are connected by a directed edge from r

3. 3 Some Terminologies Child and parent –Every node except the root has one parent –A node can have an arbitrary number of children Leaves –Nodes with no children Sibling –nodes with same parent

4. 4 Some Terminologies Path Length –number of edges on the path Depth of a node –length of the unique path from the root to that node –The depth of a tree is equal to the depth of the deepest leaf Height of a node –length of the longest path from that node to a leaf –all leaves are at height 0 –The height of a tree is equal to the height of the root Ancestor and descendant –Proper ancestor and proper descendant

5. 5 Binary Trees A tree in which no node can have more than two children The depth of an “average” binary tree is considerably smaller than N, eventhough in the worst case, the depth can be as large as N – 1.

6. 6 Example: Expression Trees Leaves are operands (constants or variables) The other nodes (internal nodes) contain operators Will not be a binary tree if some operators are not binary

7. 7 Binary Search Trees Stores keys in the nodes in a way so that searching, insertion and deletion can be done efficiently. Binary search tree property –For every node X, all the keys in its left subtree are smaller than the key value in X, and all the keys in its right subtree are larger than the key value in X

8. 8 Binary Search Trees A binary search tree Not a binary search tree

9. 9 Binary search trees Average depth of a node is O(log N); maximum depth of a node is O(N) Two binary search trees representing the same set:

10. 10 Searching BST If we are searching for 15, then we are done. If we are searching for a key < 15, then we should search in the left subtree. If we are searching for a key > 15, then we should search in the right subtree.

11. 11

12. 12 Binary Tree Node Data data r node struct leaf { int data; leaf *l; leaf *r; }; l

13. 13 #include #define YES 1 #define NO 0 class tree { private: struct leaf { int data; leaf *l; leaf *r; }; struct leaf *p; public: tree(); ~tree(); void destruct(leaf *q); void findparent(int n,int &found,leaf* &parent); void findfordel(int n,int &found,leaf *&parent,leaf* &x); void add(int n); void transverse(); void in(leaf *q); void pre(leaf *q); void post(leaf *q); void del(int n); };

14. 14 Constructor tree::tree() { p=NULL; }

15. 15 Destructor Function tree::~tree() { destruct(p); } void tree::destruct(leaf *q) { if(q!=NULL) { destruct(q->l); del(q->data); destruct(q->r); }

16. 16 Findparent() Function void tree::findparent(int n,int &found,leaf *&parent) { leaf *q; found=NO; parent=NULL; if(p==NULL) return; q=p; while(q!=NULL) { if(q->data==n) { found=YES; return; } if(q->data>n) { parent=q; q=q->l; } else { parent=q; q=q->r; }

17. 17 Adding a new element (node) Proceed down the tree as you would with a find If X is found, do nothing (or update something) Otherwise, insert X at the last spot on the path traversed Time complexity = O(height of the tree)

18. 18 Add( ) Function void tree::add(int n) { int found; leaf *t,*parent; findparent(n,found,parent); if(found==YES) cout<<"\nSuch a Node Exists"; else { t=new leaf; t->data=n; t->l=NULL; t->r=NULL; if(parent==NULL) p=t; else parent->data > n ? parent->l=t : parent->r=t; }

19. 19 Deleting an element When we delete a node, we need to consider how we take care of the children of the deleted node. –This has to be done such that the property of the search tree is maintained.

20. 20 Delete Cases Three cases: (1) the node is a leaf –Delete it immediately (2) the node has one child –Adjust a pointer from the parent to bypass that node

21. 21 (3) the node has 2 children –replace the key of that node with the minimum element at the right subtree –delete the minimum element Has either no child or only right child because if it has a left child, that left child would be smaller and would have been chosen. So invoke case 1 or 2. –Time complexity = O(height of the tree)

22. 22 Findfordel() Function void tree::findfordel(int n,int &found,leaf *&parent,leaf *&x) { leaf *q; found=0; parent=NULL; if(p==NULL) return; q=p; while(q!=NULL) { if(q->data==n) { found=1; x=q; return; } if(q->data>n) { parent=q; q=q->l; } else { parent=q; q=q->r; }

23. 23 Del() function void tree::del(int num) { leaf *parent,*x,*xsucc; int found; // If EMPTY TREE if(p==NULL) { cout<<"\nTree is Empty"; return; } parent=x=NULL; findfordel(num,found,parent,x); if(found==0) { cout<<"\nNode to be deleted NOT FOUND"; return; } // If the node to be deleted has 2 leaves if(x->l != NULL && x->r != NULL) { parent=x; xsucc=x->r; while(xsucc->l != NULL) { parent=xsucc; xsucc=xsucc->l; } x->data=xsucc->data; x=xsucc; }

24. 24 // if the node to be deleted has no child if(x->l == NULL && x->r == NULL) { if(parent->r == x) parent->r=NULL; else parent->l=NULL; delete x; return; } // if node has only right leaf if(x->l == NULL && x->r != NULL ) { if(parent->l == x) parent->l=x->r; else parent->r=x->r; delete x; return; } // if node to be deleted has only left child if(x->l != NULL && x->r == NULL) { if(parent->l == x) parent->l=x->l; else parent->r=x->l; delete x; return; }

25. 25 TreeTraversal void tree::transverse() { int c; cout<<"\n1.InOrder\n2.Preorder\n3.Postorder\nChoice: "; cin>>c; switch(c) { case 1: in(p); break; case 2: pre(p); break; case 3: post(p); break; }

26. 26 Inorder void tree::in(leaf *q) { if(q!=NULL) { in(q->l); cout data<<endl; in(q->r); }

27. 27 Preorder void tree::pre(leaf *q) { if(q!=NULL) { cout data<<endl; pre(q->l); pre(q->r); }

28. 28 Postorder void tree::post(leaf *q) { if(q!=NULL) { post(q->l); post(q->r); cout data<<endl; }