1. Chapter 10 Trees and Binary Trees Part 2

2. ?Traversal level by level

3. Definitions

6. Rooted trees with four vertices (Root is at the top of tree.)

7. Ordered trees with four vertices

8. Implementations of Ordered Trees Multiple links first child and next sibling links Correspondence with binary trees datachild1Child2 … datafirst childNext sibling

11. Conversion (from forest/Orchard to binary tree Corresponded binary tree respectively Corresponded binary tree

12. Huffman Tree( 哈夫曼树 ) Path Length ( 路径长度 ) Path Length of the binary tree Total length of all from leaves to root

13. Weighted Path Length (WPL, 带权路径长度 ) 树的带权路径长度是树的各叶结点所带的权值 与该结点到根的路径长度的乘积的和。

14. How about the WPLof the following binary trees

15. Huffman tree A (extended) binary tree with minimal WPL 。 A (extended) binary tree with minimal WPL 。

16. Processes of huffman tree

17. Huffman coding Compression suppose we have a message ： CAST CAST SAT AT A TASA CAST CAST SAT AT A TASA alphabet ={ C, A, S, T } ， frequency of them ( 次 数 ) are W ＝ { 2, 7, 4, 5 } 。 alphabet ={ C, A, S, T } ， frequency of them ( 次 数 ) are W ＝ { 2, 7, 4, 5 } 。 equal length coding first case equal length coding A : 00 T : 10 C : 01 S : 11 A : 00 T : 10 C : 01 S : 11 Total coding length of the message is ( 2+7+4+5 ) * 2 = 36. ( 2+7+4+5 ) * 2 = 36.

18. A : 0 T : 10 C : 110 S : 111 A : 0 T : 10 C : 110 S : 111 Total length of huffman coding ： 7*1+5*2+( 2+4 )*3 = 35 Which is shorter than that of equal length coding 。 霍夫曼编码是一种无前缀编码。解码时不会混淆。 Huffman tree ??

19. Binary Search Trees Can we find an implementation for ordered lists in which we can search quickly (as with binary search on a contiguous list) and in which we can make insertions and deletions quickly (as with a linked list)?

20. DEFINITION A binary search tree is a binary tree that is either empty or in which the data entry of every node has a key and satisfies the conditions: 1. The key of the left child of a node (if it exists) is less than the key of its parent node. 2. The key of the right child of a node (if it exists) is greater than the key of its parent node. 3. The left and right subtrees of the root are again binary search trees. We always require: No two entries in a binary search tree may have equal keys.

21. different views We can regard binary search trees as a new ADT. We may regard binary search trees as a specialization of binary trees. We may study binary search trees as a new implementation of the ADT ordered list.

22. The Binary Search Tree Class

23. Recursive auxiliary function: template Binary node *Search tree :: search for node( Binary node * sub root, const Record &target) const { if (sub root == NULL || sub root->data == target) return sub root; else if (sub root->data < target) return search for node(sub root->right, target); else return search for node(sub root->left, target); }

24. Nonrecursive version: template Binary node *Search tree :: search for node( Binary node *sub root, const Record &target) const { while (sub root != NULL && sub root->data != target) if (sub root->data right; else sub root = sub root->left; return sub root; }

25. Public method for tree search: template Error code Search tree :: tree search(Record &target) const { Error code result = success; Binary node *found = search for node(root, target); if (found == NULL) result = not present; else target = found->data; return result; }

26. Binary Search Trees with the Same Keys

27. search

28. Creating a BST by insertion

29. insertion

30. Method for Insertion

32. Analysis of insertion

33. Treesort When a binary search tree is traversed in inorder, the keys will come out in sorted order. This is the basis for a sorting method, called treesort: Take the entries to be sorted, use the method insert to build them into a binary search tree, and then use inorder traversal to put them out in order.

34. Treesort

35. Comparison First advantage of treesort over quicksort: The nodes need not all be available at the start of the process, but are built into the tree one by one as they become available. Second advantage: The search tree remains available for later insertions and removals. Drawback: If the keys are already sorted, then treesort will be a disasteróthe search tree it builds will reduce to a chain. Treesort should never be used if the keys are already sorted, or are nearly so.

36. Removal from a Binary Search Tree

37. Removal from a Binary Search Tree (continue)

38. Height Balance: AVL Trees Definition: An AVL tree is a binary search tree in which the heights of the left and right subtrees of the root differ by at most 1 and in which the left and right subtrees are again AVL trees. With each node of an AVL tree is associated a balance factor that is left higher, equal, or right higher according, respectively, as the left subtree has height greater than, equal to, or less than that of the right subtree.

39. Example AVL trees

41. Example non-AVL trees

43. Insertions into an AVL tree

45. Rotations of an AVL Tree

46. Double Rotation

47. Deletion with no rotations

48. Deletion with single left rotations

49. Deletion with double rotation

50. Worst-Case AVL Trees

51. Fibonacci Trees

52. Analysis of Fibonacci Trees

54. Multiway Search Trees An m-way search tree is a tree in which, for some integer m called the order of the tree, each node has at most m children.

55. Balanced Multiway Trees (B-Trees)

56. B-Tree Example

57. Insertion into a B-Tree In contrast to binary search trees, B-trees are not allowed to grow at their leaves; instead, they are forced to grow at the root. General insertion method: 1. Search the tree for the new key. This search (if the key is truly new) will terminate in failure at a leaf. 2. Insert the new key into to the leaf node. If the node was not previously full, then the insertion is finished.

58. Insertion into a B-Tree 3. When a key is added to a full node, then the node splits into two nodes, side by side on the same level, except that the median key is not put into either of the two new nodes. 4. When a node splits, move up one level, insert the median key into this parent node, and repeat the splitting process if necessary. 5. When a key is added to a full root, then the root splits in two and the median key sent upward becomes a new root. This is the only time when the B-tree grows in height.

59. Growth of a B-Tree

63. Deletion from a B-Tree