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Mastering Spark for Data Science

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

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785882142
Length 560 pages
Edition 1st Edition
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Authors (5):
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 Bifet Bifet
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Bifet
 Morgan Morgan
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Morgan
 Amend Amend
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Amend
 Hallett Hallett
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Hallett
 George George
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George
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Table of Contents (22) Chapters Close

Mastering Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
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

Story mutation


We now have enough material to enter the heart of the subject. We were able to detect near-duplicate events and group similar articles within a story. In this section, we will be working in real time (on a Spark Streaming context), listening for news articles, grouping them into stories, but also looking at how these stories may change over time. We appreciate that the number of stories is undefined as we do not know in advance what events may arise in the coming days. As optimizing KMeans for each batch interval (15 mn in GDELT) would not be ideal, neither would it be efficient, we decided to take this constraint not as a limiting factor but really as an advantage in the detection of breaking news articles.

The Equilibrium state

If we were to divide the world's news articles into say 10 or 15 clusters, and fix that number to never change over time, then training a KMeans clustering should probably group similar (but not necessarily duplicate) articles into generic stories....

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