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Note for Data Structure using C - DS By RAVI TEJA REDDY

  • Data Structure using C - DS
  • Note
  • Jawaharlal Nehru Technological University Anantapur (JNTU) College of Engineering (CEP), Pulivendula, Pulivendula, Andhra Pradesh, India - JNTUACEP
  • Computer Science Engineering
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CONTENTS CHAPTER 1 BASIC CONCEPTS 1.1 1.2 1.3. 1.4. 1.5. 1.6. 1.7. 1.8. 1.9. 1.10. 1.11. Introduction to Data Structures Data structures: organizations of data Abstract Data Type (ADT) Selecting a data structure to match the operation Algorithm Practical Algorithm design issues Performance of a program Classification of Algorithms Complexity of Algorithms Rate of Growth Analyzing Algorithms Exercises Multiple Choice Questions CHAPTER 2 RECURSION 2.1. 2.2. 2.3. 2.4. 2.5. 2.6. 2.7. 2.8. Introduction to Recursion Differences between recursion and iteration Factorial of a given number The Towers of Hanoi Fibonacci Sequence Problem Program using recursion to calculate the NCR of a given number Program to calculate the least common multiple of a given number Program to calculate the greatest common divisor Exercises Multiple Choice Questions CHAPTER 3 LINKED LISTS 3.1. 3.2. 3.3. Linked List Concepts Types of Linked Lists Single Linked List 3.3.1. Source Code for the Implementation of Single Linked List 3.4. Using a header node 3.5. Array based linked lists 3.6. Double Linked List 3.6.1. A Complete Source Code for the Implementation of Double Linked List 3.7. Circular Single Linked List 3.7.1. Source Code for Circular Single Linked List 3.8. Circular Double Linked List 3.8.1. Source Code for Circular Double Linked List 3.9. Comparison of Linked List Variations 3.10. Polynomials 3.10.1. Source code for polynomial creation with help of linked list 3.10.2. Addition of Polynomials 3.10.3. Source code for polynomial addition with help of linked list: Exercise Multiple Choice Questions I

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CHAPTER 4 STACK AND QUEUE 4.1. Stack 4.1.1. Representation of Stack 4.1.2. Program to demonstrate a stack, using array 4.1.3. Program to demonstrate a stack, using linked list 4.2. Algebraic Expressions 4.3. Converting expressions using Stack 4.3.1. Conversion from infix to postfix 4.3.2. Program to convert an infix to postfix expression 4.3.3. Conversion from infix to prefix 4.3.4. Program to convert an infix to prefix expression 4.3.5. Conversion from postfix to infix 4.3.6. Program to convert postfix to infix expression 4.3.7. Conversion from postfix to prefix 4.3.8. Program to convert postfix to prefix expression 4.3.9. Conversion from prefix to infix 4.3.10. Program to convert prefix to infix expression 4.3.11. Conversion from prefix to postfix 4.3.12. Program to convert prefix to postfix expression 4.4. Evaluation of postfix expression 4.5. Applications of stacks 4.6. Queue 4.6.1. Representation of Queue 4.6.2. Program to demonstrate a Queue using array 4.6.3. Program to demonstrate a Queue using linked list 4.7. Applications of Queue 4.8. Circular Queue 4.8.1. Representation of Circular Queue 4.9. Deque 4.10. Priority Queue Exercises Multiple Choice Questions CHAPTER 5 BINARY TREES 5.1. 5.2. 5.3. 5.4. 5.5. 5.6. 5.7. 5.8. 5.9. Trees Binary Tree Binary Tree Traversal Techniques 5.3.1. Recursive Traversal Algorithms 5.3.2. Building Binary Tree from Traversal Pairs 5.3.3. Binary Tree Creation and Traversal Using Arrays 5.3.4. Binary Tree Creation and Traversal Using Pointers 5.3.5. Non Recursive Traversal Algorithms Expression Trees 5.4.1. Converting expressions with expression trees Threaded Binary Tree Binary Search Tree General Trees (m-ary tree) 5.7.1. Converting a m-ary tree (general tree) to a binary tree Search and Traversal Techniques for m-ary trees 5.8.1. Depth first search 5.8.2. Breadth first search Sparse Matrices Exercises Multiple Choice Questions II

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CHAPTER 6 GRAPHS 6.1. 6.2. 6.3. 6.4. 6.5. Introduction to Graphs Representation of Graphs Minimum Spanning Tree 6.3.1. Kruskal’s Algorithm 6.3.2. Prim’s Algorithm Reachability Matrix Traversing a Graph 6.5.1. Breadth first search and traversal 6.5.2. Depth first search and traversal Exercises Multiple Choice Questions CHAPTER 7 SEARCHING AND SORTING 7.1. 7.2. 7.3. 7.4. 7.5. 7.6. 7.7. 7.8. Linear Search 7.1.1. A non-recursive program for Linear Search 7.1.1. A Recursive program for linear search Binary Search 7.1.2. A non-recursive program for binary search 7.1.3. A recursive program for binary search Bubble Sort 7.3.1. Program for Bubble Sort Selection Sort 7.4.1 Non-recursive Program for selection sort 7.4.2. Recursive Program for selection sort Quick Sort 7.5.1. Recursive program for Quick Sort Priority Queue and Heap and Heap Sort 7.6.2. Max and Min Heap data structures 7.6.2. Representation of Heap Tree 7.6.3. Operations on heap tree 7.6.4. Merging two heap trees 7.6.5. Application of heap tree Heap Sort 7.7.1. Program for Heap Sort Priority queue implementation using heap tree Exercises Multiple Choice Questions References and Selected Readings Index III

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Chapter 1 Basic Concepts The term data structure is used to describe the way data is stored, and the term algorithm is used to describe the way data is processed. Data structures and algorithms are interrelated. Choosing a data structure affects the kind of algorithm you might use, and choosing an algorithm affects the data structures we use. An Algorithm is a finite sequence of instructions, each of which has a clear meaning and can be performed with a finite amount of effort in a finite length of time. No matter what the input values may be, an algorithm terminates after executing a finite number of instructions. 1.1. Introduction to Data Structures: Data structure is a representation of logical relationship existing between individual elements of data. In other words, a data structure defines a way of organizing all data items that considers not only the elements stored but also their relationship to each other. The term data structure is used to describe the way data is stored. To develop a program of an algorithm we should select an appropriate data structure for that algorithm. Therefore, data structure is represented as: Algorithm + Data structure = Program A data structure is said to be linear if its elements form a sequence or a linear list. The linear data structures like an array, stacks, queues and linked lists organize data in linear order. A data structure is said to be non linear if its elements form a hierarchical classification where, data items appear at various levels. Trees and Graphs are widely used non-linear data structures. Tree and graph structures represents hierarchial relationship between individual data elements. Graphs are nothing but trees with certain restrictions removed. Data structures are divided into two types: • • Primitive data structures. Non-primitive data structures. Primitive Data Structures are the basic data structures that directly operate upon the machine instructions. They have different representations on different computers. Integers, floating point numbers, character constants, string constants and pointers come under this category. Non-primitive data structures are more complicated data structures and are derived from primitive data structures. They emphasize on grouping same or different data items with relationship between each data item. Arrays, lists and files come under this category. Figure 1.1 shows the classification of data structures. Lecture Notes 1 Dept. of Information Technology

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