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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

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Length 358 pages
Edition 2nd Edition
Languages
Concepts
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Author (1):
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Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
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Table of Contents (20) Chapters Close

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...

Attributing documents to authors


Authorship analysis has a background in stylometry, which is the study of an author's style of writing. The concept is based on the idea that everyone learns language slightly differently, and that measuring these nuances in people's writing will enable us to tell them apart using only the content of their writing.

Authorship analysis has historically (pre-1990) been performed using repeatable manual analysis and statistics, which is a good indication that it could be automated with data mining. Modern authorship analysis studies are almost entirely data mining-based, although quite a significant amount of work is still done with more manually driven analysis using linguistic styles and stylometrics. Many of the advances in feature engineering today are driven by advances in stylometrics. In other words, manual analysis discovers new features, which are then codified and used as part of the data mining process.

A key underlying feature of stylometry is that...

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