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Clojure Data Analysis Cookbook - Second Edition

You're reading from   Clojure Data Analysis Cookbook - Second Edition Dive into data analysis with Clojure through over 100 practical recipes for every stage of the analysis and collection process

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781784390297
Length 372 pages
Edition 2nd Edition
Languages
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Author (1):
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Eric Richard Rochester Eric Richard Rochester
Author Profile Icon Eric Richard Rochester
Eric Richard Rochester
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Toc

Table of Contents (14) Chapters Close

Preface 1. Importing Data for Analysis FREE CHAPTER 2. Cleaning and Validating Data 3. Managing Complexity with Concurrent Programming 4. Improving Performance with Parallel Programming 5. Distributed Data Processing with Cascalog 6. Working with Incanter Datasets 7. Statistical Data Analysis with Incanter 8. Working with Mathematica and R 9. Clustering, Classifying, and Working with Weka 10. Working with Unstructured and Textual Data 11. Graphing in Incanter 12. Creating Charts for the Web Index

Scaling document frequencies by document size

While raw token frequencies can be useful, they often have one major problem: comparing frequencies with different documents is complicated if the document sizes are not the same. If the word customer appears 23 times in a 500-word document and it appears 40 times in a 1,000-word document, which one do you think is more focused on that word? It's difficult to say.

To work around this, it's common to scale the tokens frequencies for each document by the size of the document. That's what we'll do in this recipe.

Getting ready

We'll continue building on the previous recipes in this chapter. Because of that, we'll use the same project.clj file:

(defproject com.ericrochester/text-data "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.6.0"]
                 [clojure-opennlp "0.3.2"]])

We'll use the token frequencies that we figured from the Getting document frequencies recipe. We...

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