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Learning Data Mining with Python, - Second Edition

You're reading from Learning Data Mining with Python, - Second Edition

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781787126787

Pages 358 pages

Edition 2nd Edition

Languages

Concepts

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

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...

Character n-grams

We saw how function words can be used as features to predict the author of a document. Another feature type is character n-grams. An n-gram is a sequence of n tokens, where n is a value (for text, generally between 2 and 6). Word n-grams have been used in many studies, usually relating to the topic of the documents - as per the previous chapter. However, character n-grams have proven to be of high quality for authorship attribution.

Character n-grams are found in text documents by representing the document as a sequence of characters. These n-grams are then extracted from this sequence and a model is trained. There are a number of different models for this, but a standard one is very similar to the bag-of-words model we have used earlier.

For each distinct n-gram in the training corpus, we create a feature for it. An example of an n-gram is <e t>, which is the letter e, space, and then the letter t (the angle brackets are used to denote the start and end of the n-gram...

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