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# 09 ds and algorithm session_13

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• In this slide you need to show the calculation to determine the sum of an arithmetic progression for bubble sort algorithm. Refer to student guide.
• In this slide you need to show the calculation to determine the sum of an arithmetic progression for bubble sort algorithm. Refer to student guide.
• Explain that the node marked 0 is stored at index 0 in the array, the node marked 1 is stored at index 1 in the array, and so on.
• After explaining inorder traversal in detail, give the student the concept behind preorder and postorder traversal sequence, and ask the student to write the preorder and postorder traversal sequence of the tree on its own.
• In this slide you need to show the calculation to determine the sum of an arithmetic progression for bubble sort algorithm. Refer to student guide.
• from the graphics, the student might think that a binary search tree is used to store only integer values. To avoid any such confusion, tell the students that a binary search tree can store values of any data type. For example, you can implement a binary search tree to store string values, such as names of the students in a class.
• Whenever you search a word in a dictionary, your brain applies the mechanics of a binary search tree. To look the meaning of a word, you randomly open any page of the dictionary. Now depending upon the word to be searched, you turn the pages left or right. This is same as that of a binary search tree
• In this slide you need to show the calculation to determine the sum of an arithmetic progression for bubble sort algorithm. Refer to student guide.
• In this activity, you need to write a program to implement insert and traverse operations on a binary search tree that contains the words in a dictionary. You can use a data file provided to you, instead of typing the complete code. The data file that stores the complete program is stored at the given location: TIRM  Datafiles for Faculty  Chapter 08  Activities  BinarySearchTree_CSharp.txt TIRM  Datafiles for Faculty  Chapter 08  Activities  BinarySearchTree_C++.txt Also explain the program to students.
• ### 09 ds and algorithm session_13

1. 1. Data Structures and AlgorithmsObjectives In this session, you will learn to: Store data in a tree Implement a binary tree Implement a binary search tree Ver. 1.0 Session 13
2. 2. Data Structures and AlgorithmsStoring Data in a Tree Consider structure where you are required to represent Directory a scenario the directory structure of your operating system. The directory structure contains various folders and files. A folder may further contain any number of sub folders and files. In such a case, it is not possible to represent the structure linearly because all the items have a hierarchical relationship among themselves. In such a case, it would be good if you have a data structure that enables you to store your data in a nonlinear fashion. Ver. 1.0 Session 13
3. 3. Data Structures and AlgorithmsDefining Trees A tree are used in applications in which represent a between Trees is a nonlinear data structure that the relation hierarchical relationship be represented in adata elements. data elements needs to among the various hierarchy. A B C D E F G H I J K L M Ver. 1.0 Session 13
4. 4. Data Structures and AlgorithmsDefining Trees (Contd.) The topmost node in a is referred to as a Each element in a tree tree is called root. node. root A B C D E F G H I J K L M node Ver. 1.0 Session 13
5. 5. Data Structures and AlgorithmsDefining Trees (Contd.) Each node in a tree can further have subtrees below its hierarchy. root A B C D E F G H I J K L M node Ver. 1.0 Session 13
6. 6. Data Structures and AlgorithmsTree Terminology • Let us discuss various a node with nomost frequently used Leaf node: It refers to terms that are children. with trees. A Nodes E, F, G, H, I, J, L, and M are leaf nodes. B C D E F G H I J K L M Ver. 1.0 Session 13
7. 7. Data Structures and AlgorithmsTree Terminology (Contd.) • Children A portion ofThe roots of the subtrees of aas a are Subtree: of a node: a tree, which can be viewed node called thetree in itself the node.a subtree. separate children of is called A subtree can also contain just one node called the leaf node. A E, F, G, and H are Tree with root B, containing nodesB. B children of node E, F, B C D G,the parent of these of is and H is a subtree nodes. node A. E F G H I J K L M Ver. 1.0 Session 13
8. 8. Data Structures and AlgorithmsTree Terminology (Contd.) • Degree of a from the refers to thechild node is referredof a Edge: A link node: It parent to a number of subtrees to as node in a tree. an edge. A Degree of node C is 1 Degree of node D is 2 B C D Degree of node A is 3 Edge Degree of node B is 4 E F G H I J K L M Ver. 1.0 Session 13
9. 9. Data Structures and AlgorithmsTree Terminology (Contd.) • Siblings/Brothers: It refers to the children of the same node. A Nodes B, C, and D are siblings of each other. B C D Nodes E, F, G, and H are siblings of each other. E F G H I J K L M Ver. 1.0 Session 13
10. 10. Data Structures and AlgorithmsTree Terminology (Contd.) • Level of node: It refers to to the distance (in number of Internal a node: It refers any node between the root and a leaf node. nodes) of a node from the root. Root always lies at level 0. As you move down the tree, the level increases by one. Level 0 A Nodes B, C, D, and K are internal nodes. Level 1 B C D E F G H I J K Level 2 L M Level 3 Ver. 1.0 Session 13
11. 11. Data Structures and AlgorithmsTree Terminology (Contd.) • Depth of a tree: Refers to the total number of levels in the tree. The depth of the following tree is 4. Level 0 A Level 1 B C D E F G H I J K Level 2 L M Level 3 Ver. 1.0 Session 13
12. 12. Data Structures and AlgorithmsJust a minute Consider the following tree and answer the questions that follow: a. What is the depth of the tree? b. Which nodes are children of node B? c. Which node is the parent of node F? root d. What is the level of node E? e. Which nodes are the siblings of node H? A f. Which nodes are the siblings of node D? g. Which nodes are leaf nodes? B C D E F G H I Ver. 1.0 Session 13
13. 13. Data Structures and AlgorithmsJust a minute Answer: a. 4 b. D and E c. C d. 2 e. H does not have any siblings f. The only sibling of D is E g. F, G, H, and I Ver. 1.0 Session 13
14. 14. Data Structures and AlgorithmsDefining Binary Trees Binary binary tree: Strictly tree is a specific type of tree in which each node can have binary tree in children namely left child and right child. A at most two which every node, except for the leaf nodes, There are variousleft and of binary trees: has non-empty types right children. Strictly binary tree Full binary tree Complete binary tree Ver. 1.0 Session 13
15. 15. Data Structures and AlgorithmsDefining Binary Trees (Contd.) Full binary tree: d A binary tree of depth d that contains exactly 2 – 1 nodes. A Depth = 3 Total number of 3 nodes = 2 – 1 = 7 B C D E F G Ver. 1.0 Session 13
16. 16. Data Structures and AlgorithmsDefining Binary Trees (Contd.) Complete binary tree: A binary tree with n nodes and depth d whose nodes correspond to the nodes numbered from 0 to n − 1 in the full binary tree of depth k. 0 0 0 A A A 1 B 2 C 1 B 2 C 1 B 2 C3 4 5 6 3 4 5 3 4 5 D E F G D E F D E G Full Binary Tree Complete Binary Tree Incomplete Binary Tree Ver. 1.0 Session 13
17. 17. Data Structures and AlgorithmsRepresenting a Binary Tree Array representation of binary trees: If there are n nodes in a binary All the nodes are represented as the elements for any node with index tree, then of an array. i, where 0 < i < n – 1: Parent of i is at (i – 1)/2. A [0] Left child of i is at 2i + 1: – If 2i + 1 > n – 1, then B [1] the node does not 0 have a left child. C [2] Right child of i is at 2i + 2: A – If 2i + 2 > n – 1, then D [3] the node does have a 1 2 right child. E [4] B C 3 4 5 6 F [5] D E F G G [6] Binary Tree Array Representation Ver. 1.0 Session 13
18. 18. Data Structures and AlgorithmsRepresenting a Binary Tree (Contd.) Linked representation of a binary tree: It uses a linked list to implement a binary tree. Each node in the linked representation holds the following information: – Data – Reference to the left child – Reference to the right child – If a node does not have a left child or a right child or both, the respective left or right child fields of that node point to NULL. Data Node Ver. 1.0 Session 13
19. 19. Data Structures and AlgorithmsRepresenting a Binary Tree (Contd.) root root 52 . 52 . 36 68 . 36 . 68 . 24 59 72 24 59 . 72 . 70 80 . 70 80 Binary Tree Linked Representation Ver. 1.0 Session 13
20. 20. Data Structures and AlgorithmsTraversing a Binary Tree You can implement various operations on a binary tree. A common operation on a binary tree is traversal. Traversal refers to the process of visiting all the nodes of a binary tree once. There are three ways for traversing a binary tree: Inorder traversal Preorder traversal Postorder traversal Ver. 1.0 Session 13
21. 21. Data Structures and AlgorithmsInorder Traversal Steps for traversing a tree in inorder sequence are as follows: 1. Traverse the left subtree 2. Visit root 3. Traverse the right subtree • Let us consider an example. Ver. 1.0 Session 13
22. 22. Data Structures and AlgorithmsInorder Traversal (Contd.) The left subtree of node B is not NULL. A Therefore, move to node D to traverse the left B subtree of B. A. root A B C D E F G I H Ver. 1.0 Session 13
23. 23. Data Structures and AlgorithmsInorder Traversal (Contd.) The left subtree of node D is NULL. Therefore, visit node D. root A B C D E F G I H D Ver. 1.0 Session 13
24. 24. Data Structures and AlgorithmsInorder Traversal (Contd.) Right subtree of D is not NULL Therefore, move to the right subtree of node D root A B C D E F G I H D Ver. 1.0 Session 13
25. 25. Data Structures and AlgorithmsInorder Traversal (Contd.) Left subtree of H is empty. Therefore, visit node H. root A B C D E F G I H D H Ver. 1.0 Session 13
26. 26. Data Structures and AlgorithmsInorder Traversal (Contd.) Right subtree of H is empty. Therefore, move to node B. root A B C D E F G I H D H Ver. 1.0 Session 13
27. 27. Data Structures and AlgorithmsInorder Traversal (Contd.) The left subtree of B has been visited. Therefore, visit node B. root A B C D E F G I H D H B Ver. 1.0 Session 13
28. 28. Data Structures and AlgorithmsInorder Traversal (Contd.) Right subtree of B is not empty. Therefore, move to the right subtree of B. root A B C D E F G I H D H B Ver. 1.0 Session 13
29. 29. Data Structures and AlgorithmsInorder Traversal (Contd.) Left subtree of E is empty. Therefore, visit node E. root A B C D E F G I H D H B E Ver. 1.0 Session 13
30. 30. Data Structures and AlgorithmsInorder Traversal (Contd.) Right subtree of E is empty. Therefore, move to node A. root A B C D E F G I H D H B E Ver. 1.0 Session 13
31. 31. Data Structures and AlgorithmsInorder Traversal (Contd.) Left subtree of A has been visited. Therefore, visit node A. root A B C D E F G I H D H B E A Ver. 1.0 Session 13
32. 32. Data Structures and AlgorithmsInorder Traversal (Contd.) Right subtree of A is not empty. Therefore, move to the right subtree of A. root A B C D E F G I H D H B E A Ver. 1.0 Session 13
33. 33. Data Structures and AlgorithmsInorder Traversal (Contd.) Left subtree of C is not empty. Therefore, move to the left subtree of C. root A B C D E F G I H D H B E A Ver. 1.0 Session 13
34. 34. Data Structures and AlgorithmsInorder Traversal (Contd.) Left subtree of F is empty. Therefore, visit node F. root A B C D E F G I H D H B E A F Ver. 1.0 Session 13
35. 35. Data Structures and AlgorithmsInorder Traversal (Contd.) Right subtree of F is empty. Therefore, move to node C. root A B C D E F G I H D H B E A F Ver. 1.0 Session 13
36. 36. Data Structures and AlgorithmsInorder Traversal (Contd.) The left subtree of node C has been visited. Therefore, visit node C. root A B C D E F G I H D H B E A F C Ver. 1.0 Session 13
37. 37. Data Structures and AlgorithmsInorder Traversal (Contd.) Right subtree of C is not empty. Therefore, move to the right subtree of node C. root A B C D E F G I H D H B E A F C Ver. 1.0 Session 13
38. 38. Data Structures and AlgorithmsInorder Traversal (Contd.) Left subtree of G is not empty. Therefore, move to the left subtree of node G. root A B C D E F G I H D H B E A F C Ver. 1.0 Session 13
39. 39. Data Structures and AlgorithmsInorder Traversal (Contd.) Left subtree of I is empty. Therefore, visit I. root A B C D E F G I H D H B E A F C I Ver. 1.0 Session 13
40. 40. Data Structures and AlgorithmsInorder Traversal (Contd.) Right subtree of I is empty. Therefore, move to node G. root A B C D E F G I H D H B E A F C I Ver. 1.0 Session 13
41. 41. Data Structures and AlgorithmsInorder Traversal (Contd.) Visit node G. root A B C D E F G I H D H B E A F C I G Ver. 1.0 Session 13
42. 42. Data Structures and AlgorithmsInorder Traversal (Contd.) Right subtree of G is empty. root A B C Traversal complete D E F G I H D H B E A F C I G Ver. 1.0 Session 13
43. 43. Data Structures and AlgorithmsPreorder Traversal Steps for traversing a tree in preorder sequence are as follows: 1. Visit root 2. Traverse the left subtree 3. Traverse the right subtree Ver. 1.0 Session 13
44. 44. Data Structures and AlgorithmsPreorder Traversal (Contd.) Perform the preorder traversal of the following tree. A B C D E F G I H Preorder Traversal: A B D H E C F G I Ver. 1.0 Session 13
45. 45. Data Structures and AlgorithmsPostorder Traversal Steps for traversing a tree in postorder sequence are as follows: 1. Traverse the left subtree 2. Traverse the right subtree 3. Visit the root Ver. 1.0 Session 13
46. 46. Data Structures and AlgorithmsPostorder Traversal (Contd.) Perform the postorder traversal of the following tree. A B C D E F G I H Postorder Traversal: H D E B F I G C A Ver. 1.0 Session 13
47. 47. Data Structures and AlgorithmsJust a minute In _________ traversal method, root is processed before traversing the left and right subtrees. Answer: Preorder Ver. 1.0 Session 13
48. 48. Data Structures and AlgorithmsImplementing a Binary Search Tree Consider a scenario. SysCall Ltd. is a cellular phone company with millions of customers spread across the world. Each customer is assigned a unique identification number (id). Individual customer records can be accessed by referring to the respective id. These ids need to be stored in a sorted manner in such a way so that you can perform various transactions, such as retrieval, insertion, and deletion, easily. Ver. 1.0 Session 13
49. 49. Data Structures and AlgorithmsImplementing a Binary Search Tree (Contd.) Therefore, you need to implementto store structure the Which data structure will you use a data the id of that customers? provides the advantages of both arrays as well as linked lists. you implement an array? Can – Search operation in an array the advantages of both A binary search tree combines is fast. – However, insertion arrays and linked lists. and deletion in an array is complex in nature. – In this case, the total number of customer ids to be stored is very large. Therefore, insertion and deletion will be very time consuming. Can you implement a linked list? Insert and delete operation in a linked is fast. However, linked lists allow only sequential search. If you need to access a particular customer id, which is located near the end of the list, then it would require you to visit all the preceding nodes, which again can be very time consuming. Ver. 1.0 Session 13
50. 50. Data Structures and AlgorithmsDefining a Binary Search Tree The following tree example of a binary search tree. Binary search is an is a binary tree in which every node satisfies the following conditions: All values in the left subtree of a node are less than the value of the node. 52 All values in the right subtree of a node are greater than the value of the node. 36 68 24 44 59 72 40 55 Ver. 1.0 Session 13
51. 51. Data Structures and AlgorithmsDefining a Binary Search Tree (Contd.) You can implement various operations on a binary search tree: Traversal Search Insert Delete Ver. 1.0 Session 13
52. 52. Data Structures and AlgorithmsSearching a Node in a Binary Search Tree Search operation refers value, process ofto perform the a To search for a specific to the you need searching for specified steps: in the tree. following value 1. Make currentNode point to the root node 2. If currentNode is null: a. Display “Not Found” b. Exit 3. Compare the value to be searched with the value of currentNode. Depending on the result of the comparison, there can be three possibilities: a. If the value is equal to the value of currentNode: i. Display “Found” ii. Exit d. If the value is less than the value of currentNode: i. Make currentNode point to its left child ii. Go to step 2 g. If the value is greater than the value of currentNode: i. Make currentNode point to its right child ii. Go to step 2 Ver. 1.0 Session 13
53. 53. Data Structures and AlgorithmsJust a minute In a binary search tree, all the values in the left subtree of a node are _______ than the value of the node. Answer: smaller Ver. 1.0 Session 13
54. 54. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree Write an algorithmnode in a the position of tree, you firstto be Before inserting a to locate binary search a new node inserted check whether the tree.is empty or not. need to in a binary search tree If the tree is empty, make the new node as root. If the tree is not empty, you need to locate the appropriate position for the new node to be inserted. This requires you to locate the parent of the new node to be inserted. Once the parent is located, the new node is inserted as the left child or right child of the parent. To locate the parent of the new node to be inserted, you need to implement a search operation in the tree. Ver. 1.0 Session 13
55. 55. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) 1. Mark the root node as currentNode Algorithm to locate the 3. Make parent point to NULL parent of the new node to 5. Repeat steps 4, 5, and 6 until currentNode be inserted. becomes NULL 7. Make parent point to currentNode root 9. If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child 12. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
56. 56. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) 1. Mark the root node as currentNode Refer to the algorithm to Locate the position of a new node 55. 3. Make parent point to NULL locate the parent of the 5. Repeat steps 4, 5, and 6 until currentNode new node to be inserted. becomes NULL 7. Make parent point to currentNode root 9. If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
57. 57. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL • Make parent point to currentNode root • If the value of the new node is less than that of currentNode . 52 . currentNode: a. Make currentNode point to its left child 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
58. 58. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL • Make parent point to currentNode root parent = NULL • If the value of the new node is less than that of currentNode . 52 . currentNode: a. Make currentNode point to its left child 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
59. 59. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL • Make parent point to currentNode root parent = NULL • If the value of the new node is less than that of currentNode . 52 . currentNode: a. Make currentNode point to its left child 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
60. 60. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL • Make parent point to currentNode root parent = NULL parent • If the value of the new node is less than that of currentNode . 52 . currentNode: a. Make currentNode point to its left child 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
61. 61. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL 55 > 52 • Make parent point to currentNode root parent • If the value of the new node is less than that of currentNode . 52 . currentNode: a. Make currentNode point to its left child 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
62. 62. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) 1. Mark the root node as currentNode Locate the position of a new node 55. 3. Make parent point to NULL 5. Repeat steps 4, 5, and 6 until currentNode becomes NULL 55 > 52 7. Make parent point to currentNode root parent 9. If the value of the new node is less than that of currentNode . 52 . currentNode: a. Make currentNode point to its left child 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
63. 63. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) 1. Mark the root node as currentNode Locate the position of a new node 55. 3. Make parent point to NULL 5. Repeat steps 4, 5, and 6 until currentNode becomes NULL 55 > 52 7. Make parent point to currentNode root parent 9. If the value of the new node is less than that of currentNode . 52 . currentNode: a. Make currentNode point to its left child currentNode 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
64. 64. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL • Make parent point to currentNode root parent • If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child currentNode 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
65. 65. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL • Make parent point to currentNode root parent • If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child parent currentNode 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
66. 66. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL 55 < 68 • Make parent point to currentNode root • If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child parent currentNode 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . . 70 80 Ver. 1.0 Session 13
67. 67. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) 1. Mark the root node as currentNode Locate the position of a new node 55. 3. Make parent point to NULL 5. Repeat steps 4, 5, and 6 until currentNode becomes NULL 55 < 68 7. Make parent point to currentNode root 9. If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child parent currentNode 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . currentNode . 70 80 Ver. 1.0 Session 13
68. 68. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL • Make parent point to currentNode root • If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child parent 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: a. Make currentNode point to its right child 24 59 . 72 . currentNode . 70 80 Ver. 1.0 Session 13
69. 69. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL • Make parent point to currentNode root • If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child parent 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: parent a. Make currentNode point to its right child 24 59 . 72 . currentNode . 70 80 Ver. 1.0 Session 13
70. 70. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL 55 < 59 • Make parent point to currentNode root • If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: parent a. Make currentNode point to its right child 24 59 . 72 . currentNode . 70 80 Ver. 1.0 Session 13
71. 71. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) 1. Mark the root node as currentNode Locate the position of a new node 55. 3. Make parent point to NULL 5. Repeat steps 4, 5, and 6 until currentNode becomes NULL 55 < 59 7. Make parent point to currentNode root 9. If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: parent a. Make currentNode point to its right child 24 59 . 72 . currentNode currentNode = NULL . 70 80 Ver. 1.0 Session 13
72. 72. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Mark the root node as currentNode Locate the position of a new node 55. • Make parent point to NULL • Repeat steps 4, 5, and 6 until currentNode becomes NULL • Make parent point to currentNode root • If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: parent a. Make currentNode point to its right child 24 59 . 72 . currentNode = NULL . 70 80 Ver. 1.0 Session 13
73. 73. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) 1. Mark the root node as currentNode Once the parent of the new 3. Make parent point to NULL node is located, you can 5. Repeat steps 4, 5, and 6 until currentNode insert the node as the child becomes NULL of its parent. 7. Make parent point to currentNode root 9. If the value of the new node is less than that of . 52 . currentNode: a. Make currentNode point to its left child 11. If the value of the new node is greater than that . 36 . 68 . of currentNode: parent a. Make currentNode point to its right child 24 59 . 72 . currentNode = NULL . 70 80 Ver. 1.0 Session 13
74. 74. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) Write an algorithm to insert a node in a binary search tree. Ver. 1.0 Session 13
75. 75. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) 1. Allocate memory for the new node. 3. Assign value to the data field of new node. Insert 55 Algorithm to insert a node 5. Make the left and right child of the new node in a binary search tree. point to NULL. 7. Locate the node which will be the parent of the node to be inserted. Mark it as parent. root 9. If parent is NULL (Tree is empty): . 52 . a. Mark the new node as ROOT b. Exit 11. If the value in the data field of new node is . 36 . 68 . less than that of parent: a. Make the left child of parent point to the new node 24 59 . 72 . b. Exit 13. If the value in the data field of the new node is greater than that of the parent: . 70 80 a. Make the right child of parent point to the new node b. Exit Ver. 1.0 Session 13
76. 76. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Allocate memory for the new node. • Assign value to the data field of new node. • Make the left and right child of the new node point to NULL. • Locate the node which will be the parent of the node to be inserted. Mark it as parent. root • If parent is NULL (Tree is empty): . 52 . a. Mark the new node as ROOT b. Exit 11. If the value in the data field of new node is . 36 . 68 . less than that of parent: a. Make the left child of parent point to the new node 24 59 . 72 . b. Exit 13. If the value in the data field of the new node is greater than that of the parent: . 70 80 a. Make the right child of parent point to the new node b. Exit Ver. 1.0 Session 13
77. 77. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Allocate memory for the new node. • Assign value to the data field of new node. • Make the left and right child of the new node point to NULL. • Locate the node which will be the parent of the node to be inserted. Mark it as parent. root • If parent is NULL (Tree is empty): . 52 . a. Mark the new node as ROOT b. Exit 11. If the value in the data field of new node is . 36 . 68 . less than that of parent: a. Make the left child of parent point to the new node 24 59 . 72 . b. Exit 13. If the value in the data field of the new node is greater than that of the parent: 55 . 70 80 a. Make the right child of parent point to the new node b. Exit Ver. 1.0 Session 13
78. 78. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Allocate memory for the new node. • Assign value to the data field of new node. • Make the left and right child of the new node point to NULL. • Locate the node which will be the parent of the node to be inserted. Mark it as parent. root • If parent is NULL (Tree is empty): . 52 . a. Mark the new node as ROOT b. Exit 11. If the value in the data field of new node is . 36 . 68 . less than that of parent: a. Make the left child of parent point to the new node 24 59 . 72 . b. Exit 13. If the value in the data field of the new node is greater than that of the parent: 55 . 70 80 a. Make the right child of parent point to the new node b. Exit Ver. 1.0 Session 13
79. 79. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Allocate memory for the new node. • Assign value to the data field of new node. • Make the left and right child of the new node point to NULL. • Locate the node which will be the parent of the node to be inserted. Mark it as parent. root • If parent is NULL (Tree is empty): . 52 . a. Mark the new node as ROOT b. Exit 11. If the value in the data field of new node is . 36 . 68 . less than that of parent: parent a. Make the left child of parent point to the new node 24 59 . 72 . b. Exit 13. If the value in the data field of the new node is greater than that of the parent: 55 . 70 80 a. Make the right child of parent point to the new node b. Exit Ver. 1.0 Session 13
80. 80. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) • Allocate memory for the new node. • Assign value to the data field of new node. • Make the left and right child of the new node point to NULL. • Locate the node which will be the parent of the node to be inserted. Mark it as parent. root • If parent is NULL (Tree is empty): . 52 . a. Mark the new node as ROOT b. Exit 11. If the value in the data field of new node is . 36 . 68 . less than that of parent: parent a. Make the left child of parent point to the new node 24 59 . 72 . b. Exit 13. If the value in the data field of the new node is greater than that of the parent: 55 . 70 80 a. Make the right child of parent point to the new node b. Exit Ver. 1.0 Session 13
81. 81. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) 1. Allocate memory for the new node. 3. Assign value to the data field of new node. 5. Make the left and right child of the new node point to NULL. 7. Locate the node which will be the parent of the node to be inserted. Mark it as parent. root 9. If parent is NULL (Tree is empty): . 52 . a. Mark the new node as ROOT b. Exit 11. If the value in the data field of new node is . 36 . 68 . less than that of parent: parent a. Make the left child of parent point to the new node 24 59 . 72 . b. Exit 13. If the value in the data field of the new node is greater than that of the parent: 55 . 70 80 a. Make the right child of parent point to the new node b. Exit Ver. 1.0 Session 13
82. 82. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) 1. Allocate memory for the new node. 3. Assign value to the data field of new node. 5. Make the left and right child of the new node point to NULL. 7. Locate the node which will be the parent of the node to be inserted. Mark it as parent. root 9. If parent is NULL (Tree is empty): . 52 . a. Mark the new node as ROOT b. Exit 11. If the value in the data field of new node is . 36 . 68 . less than that of parent: parent • Make the left child of parent point to the new node 24 . 59 . 72 . • Exit 13. If the value in the data field of the new node is greater than that of the parent: 55 . 70 80 a. Make the right child of parent point to the new node b. Exit Ver. 1.0 Session 13
83. 83. Data Structures and AlgorithmsInserting Nodes in a Binary Search Tree (Contd.) 1. Allocate memory for the new node. 3. Assign value to the data field of new node. 5. Make the left and right child of the new node point to NULL. 7. Locate the node which will be the parent of the node to be inserted. Mark it as parent. root 9. If parent is NULL (Tree is empty): . 52 . a. Mark the new node as ROOT b. Exit 11. If the value in the data field of new node is . 36 . 68 . less than that of parent: parent • Make the left child of parent point to the new node 24 59 . 72 . • Exit 13. If the value in the data field of the new node is greater than that of the parent: 55 . 70 80 a. Make the right child of parent point to the new node b. Exit Insert operation complete Ver. 1.0 Session 13
84. 84. Data Structures and AlgorithmsDeleting Nodes from a Binary Search Tree Write an algorithm to locate the position refers node to Delete operation in a binary search tree of the to the process of deleting the specified node from the tree. deleted from a binary search tree. Before implementing a delete operation, you first need to locate the position of the node to be deleted and its parent. To locate the position of the node to be deleted and its parent, you need to implement a search operation. Ver. 1.0 Session 13
85. 85. Data Structures and AlgorithmsDeleting Nodes from a Binary Search Tree (Contd.) • Make a variable/pointer currentNode point to the ROOT node. Suppose youlocate to delete Algorithm to want the node • Make a variable/pointer parent point to NULL. node 70 to be deleted. • Repeat steps a, b, and c until currentNode becomes NULL or the value of the node to be root searched becomes equal to that of currentNode: . 52 . a. Make parent point to currentNode. c. If the value to be deleted is less than that of currentNode: . 36 . 68 . i. Make currentNode point to its left child. 24 59 . 72 . e. If the value to be deleted is greater than that of currentNode: i. Make currentNode point to its . 70 80 right child. 69 Ver. 1.0 Session 13
86. 86. Data Structures and AlgorithmsDeleting Nodes from a Binary Search Tree (Contd.) • Make a variable/pointer currentNode point to the ROOT node. Suppose you want to delete • Make a variable/pointer parent point to NULL. node 70 • Repeat steps a, b, and c until currentNode becomes NULL or the value of the node to be root searched becomes equal to that of currentNode: currentNode . 52 . a. Make parent point to currentNode. c. If the value to be deleted is less than that of currentNode: . 36 . 68 . i. Make currentNode point to its left child. 24 59 . 72 . e. If the value to be deleted is greater than that of currentNode: i. Make currentNode point to its . 70 80 right child. 69 Ver. 1.0 Session 13