10. Heaps: Ordered Trees | Everyday data structures

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Table Of Contents

Everyday data structures

By : Smith

Everyday data structures

By: Smith

Overview of this book

Explore a new world of data structures and their applications easily with this data structures book. Written by software expert William Smith, you?ll learn how to master basic and advanced data structure concepts. ? Fully understand data structures using Java, C and other common languages ? Work through practical examples and learn real-world applications ? Get to grips with data structure problem solving using case studies

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Data Types: Foundational Structures

1. Data Types: Foundational Structures

Numeric data types

Boolean data type

Strings

Summary

2. Arrays: Foundational Collections

2. Arrays: Foundational Collections

Mutable versus immutable arrays

Advanced topics

Summary

3. Lists: Linear Collections

3. Lists: Linear Collections

List implementations

Instantiating lists

Revisiting users logged in to a service

Doubly linked list

Searching

A few pointers

Summary

4. Stacks: LIFO Collections

4. Stacks: LIFO Collections

Initializing stacks

Case study: motion planning algorithm

Advanced topics - stack implementations

Summary

5. Queues: FIFO Collections

5. Queues: FIFO Collections

Initializing queues

Case study: customer service

Advanced topics

Summary

6. Dictionaries: Keyed Collections

6. Dictionaries: Keyed Collections

Initializing dictionaries

Case study: arcade ticket totals

Advanced topics

Summary

7. Sets: No Duplicates

7. Sets: No Duplicates

Set theory

Initializing sets

Example: revisiting users logged in to a service

Case-study: music playlists

Advanced topics

Summary

8. Structs: Complex Types

8. Structs: Complex Types

The essentials

Enums

Summary

9. Trees: Non-Linear Structures

9. Trees: Non-Linear Structures

Tree data structure versus tree data type

Tree terminology

Common operations

Instantiating trees

Tree structures

Recursion

Traversal

Summary

10. Heaps: Ordered Trees

10. Heaps: Ordered Trees

Heap implementations

Heap operations

Instantiating heaps

Min heap structure

Common applications

Summary

11. Graphs: Values with Relationships

11. Graphs: Values with Relationships

Visual graph concepts

Graph operations

Graph implementations

The graph data structure

Summary

12. Sorting: Bringing Order Out Of Chaos

12. Sorting: Bringing Order Out Of Chaos

Selection sort

Insertion sort

Bubble sort

Quick sort

Merge sort

Bucket sort

Summary

13. Searching: Finding What You Need

13. Searching: Finding What You Need

Linear search

Binary search

Jump search

Summary

Common applications

Heap data structures are actually quite common, although you may not always realize you are working with one. Here are some of the most common applications for the heap data structure:

Selection algorithms: A selection algorithm is used to determine the k^th smallest or largest element in a collection, or the median valued object of a collection. In a typically collection, this operation costs O(n). However, in an ordered heap implemented with an array finding the k^th element is an O(1) operation because we can find the element by simply examining the k index in the array.
Priority queue: Priority queues are an abstract data structure similar to standard queues except that the nodes contain an additional value representing the priority of that object in relation to others in the collection. Due to the natural sorting of the heap data structure, priority queues are often implemented using the heap.

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Tech Concepts

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Programming languages

73

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