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Learning Data Mining with Python

You're reading from Learning Data Mining with Python Use Python to manipulate data and build predictive models

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781787126787

Length 358 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Mining

Author (1):

Robert Layton

View More author details

Table of Contents (20) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with Data Mining FREE CHAPTER

2. Classifying with scikit-learn Estimators

3. Predicting Sports Winners with Decision Trees

4. Recommending Movies Using Affinity Analysis

5. Features and scikit-learn Transformers

6. Social Media Insight using Naive Bayes

7. Follow Recommendations Using Graph Mining

8. Beating CAPTCHAs with Neural Networks

9. Authorship Attribution

10. Clustering News Articles

11. Object Detection in Images using Deep Neural Networks

12. Working with Big Data

13. Next Steps...

Chapter 7. Follow Recommendations Using Graph Mining

Graphs can be used to represent a wide range of phenomena. This is particularly true for online social networks, and the Internet of Things (IoT). Graph mining is big business, with websites such as Facebook running on data analysis experiments performed on graphs.

Social media websites are built upon engagement. Users without active news feeds, or interesting friends to follow, do not engage with sites. In contrast, users with more interesting friends and followees engage more, see more ads. This leads to larger revenue streams for the website.

In this chapter, we look at how to define similarity on graphs, and how to use them within a data mining context. Again, this is based on a model of the phenomena. We look at some basic graph concepts, like sub-graphs and connected components. This leads to an investigation of cluster analysis, which we delve more deeply into in Chapter 10

,

Clustering News Articles.

The topics covered in this chapter...

The rest of the chapter is locked

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Authors (1)

Robert Layton

Robert Layton

Robert Layton is a data scientist investigating data-driven applications to businesses across a number of sectors. He received a PhD investigating cybercrime analytics from the Internet Commerce Security Laboratory at Federation University Australia, before moving into industry, starting his own data analytics company dataPipeline. Next, he created Eureaktive, which works with tech-based startups on developing their proof-of-concepts and early-stage prototypes. Robert also runs the LearningTensorFlow website, which is one of the world's premier tutorial websites for Google's TensorFlow library. Robert is an active member of the Python community, having used Python for more than 8 years. He has presented at PyConAU for the last four years and works with Python Charmers to provide Python-based training for businesses and professionals from a wide range of organisations. Robert can be best reached via Twitter @robertlayton

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