9. Trees: Non-Linear Structures | Everyday data structures

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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 operations

Tree data structures can consistent of 1...n nodes., which means that even a single node without a parent or any children is still considered a tree. Therefore, many of the common operations associated with trees can be defined in terms of a single node, or from the perspective of the same. Here is a list of the most common operations associated with trees

Data: The data operation is associated with a single node, and returns the object or value contained in that node.
Children: The children operation returns the collection of siblings associated with this parent node.
Parent: Some tree structures provide a mechanism to "climb" the tree, or traverse the structure from any particular node back toward the root.
Enumerate: An enumeration operation will return a list or some other collection containing every descendant of a particular node, including the root node itself.
Insert: An insert operation allows a new node to be added as a child of an existing node in the tree. The insert...

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Programming languages

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