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Practical Data Analysis Cookbook

You're reading from   Practical Data Analysis Cookbook Over 60 practical recipes on data exploration and analysis

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781783551668
Length 384 pages
Edition 1st Edition
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Author (1):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Preparing the Data FREE CHAPTER 2. Exploring the Data 3. Classification Techniques 4. Clustering Techniques 5. Reducing Dimensions 6. Regression Methods 7. Time Series Techniques 8. Graphs 9. Natural Language Processing 10. Discrete Choice Models 11. Simulations Index

Introduction


Graphs are everywhere; when you get in your car and drive around using a GPS, you perhaps do not even realize that it is solving a graph problem to get you from point A to point B over the shortest path or in the shortest time.

The origins of graph theory reach the 18th century when Leonard Euler proposed the solution to the Königsberg bridge problem. (To read more on the topic, you can refer to http://www2.gsu.edu/~matgtc/origin%20of%20graph%20theory.pdf.) From that point onward, some of the problems that were deemed unsolvable could be solved; the Internet (or even your local network) can be viewed and analyzed as a graph, scheduling problems that airlines solve can be modeled as a graph, or (as we will see) a social network is much easier to handle if we realize that it is a graph.

Graphs are structures consisting of nodes (sometimes called vertices) and edges (sometimes called arcs or lines) that connect two nodes:

The preceding example shows the simplest network possible,...

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