Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Mastering Spark for Data Science

You're reading from   Mastering Spark for Data Science Lightning fast and scalable data science solutions

Arrow left icon
Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
Arrow right icon
Authors (5):
Arrow left icon
David George David George
Author Profile Icon David George
David George
Matthew Hallett Matthew Hallett
Author Profile Icon Matthew Hallett
Matthew Hallett
Antoine Amend Antoine Amend
Author Profile Icon Antoine Amend
Antoine Amend
Andrew Morgan Andrew Morgan
Author Profile Icon Andrew Morgan
Andrew Morgan
Albert Bifet Albert Bifet
Author Profile Icon Albert Bifet
Albert Bifet
+1 more Show less
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. The Big Data Science Ecosystem FREE CHAPTER 2. Data Acquisition 3. Input Formats and Schema 4. Exploratory Data Analysis 5. Spark for Geographic Analysis 6. Scraping Link-Based External Data 7. Building Communities 8. Building a Recommendation System 9. News Dictionary and Real-Time Tagging System 10. Story De-duplication and Mutation 11. Anomaly Detection on Sentiment Analysis 12. TrendCalculus 13. Secure Data 14. Scalable Algorithms

GDELT dataset


In order to validate our implementation, we use the GDELT dataset we analyzed in the previous chapter. We extracted all of the communities and spent some time looking at the person names to see whether or not our community clustering was consistent. The full picture of the communities is reported in Figure 7 and has been realized using the Gephi software, where only the top few thousand connections have been imported:

Figure 7: Community detection on January 12

We first observe that most of the communities we detected are totally aligned with the ones we could eyeball on a force-directed layout, giving a good confidence level about the algorithm accuracy.

The Bowie effect

Any well-defined community has been properly identified, and the less obvious ones are the ones surrounding highly connected vertices such as David Bowie. The name David Bowie being heavily mentioned in GDELT articles alongside so many different persons that, on that day of January 12, 2016, it became too large...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image