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Clojure for Data Science

You're reading from   Clojure for Data Science Statistics, big data, and machine learning for Clojure programmers

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Length 608 pages
Edition 1st Edition
Languages
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Author (1):
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Henry Garner Henry Garner
Author Profile Icon Henry Garner
Henry Garner
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Table of Contents (12) Chapters Close

Preface 1. Statistics FREE CHAPTER 2. Inference 3. Correlation 4. Classification 5. Big Data 6. Clustering 7. Recommender Systems 8. Network Analysis 9. Time Series 10. Visualization Index

Chapter 5. Big Data

 

"More is different."

 
 --Philip Warren Anderson

In the previous chapters, we've used regression techniques to fit models to the data. In Chapter 3, Correlation, for example, we built a linear model that used ordinary least squares and the normal equation to fit a straight line through the athletes' heights and log weights. In Chapter 4, Classification, we used Incanter's optimize namespace to minimize the logistic cost function and build a classifier of Titanic's passengers. In this chapter, we'll apply similar analysis in a way that's suitable for much larger quantities of data.

We'll be working with a relatively modest dataset of only 100,000 records. This isn't big data (at 100 MB, it will fit comfortably in the memory of one machine), but it's large enough to demonstrate the common techniques of large-scale data processing. Using Hadoop (the popular framework for distributed computation...

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