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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 2. Cleaning and Validating Data FREE CHAPTER 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

Performing naïve Bayesian classification with MALLET

MALLET has gotten its reputation as a library for topic modeling. However, it also has a lot of other algorithms in it.

One popular algorithm that MALLET implements is naïve Bayesian classification. If you have documents that are already divided into categories, you can train a classifier to categorize new documents into those same categories. Often, this works surprisingly well.

One common use for this is in spam e-mail detection. We'll use this as our example here too.

Getting ready

We'll need to have MALLET included in our project.clj file:

(defproject com.ericrochester/text-data "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.6.0"]
                 [cc.mallet/mallet "2.0.7"]])

Just as in the Performing topic modeling with MALLET recipe, the list of classes to be included is a little long, but most of them are for the processing pipeline, as shown here:

(require '[clojure.java...
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