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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

Introduction

If concurrent processing has performance implications when structuring programs, parallel processing is a way to get better performance that has implications on how we structure our programs. Although they are often conflated, concurrent processing and parallel processing are different solutions to different problems. Concurrency is good for expressing programs that involve different tasks that can be, or must be, carried out at the same time. Parallelization is a good option if you want to perform the same task many times, all at once. Parallelization is not necessary, but it can help tremendously with your program's performance.

Earlier, the easiest, and often best, strategy to improve performance was to go on a vacation. Moore's law implies that processor speed will double approximately every 18 months, so in the 1990s, we could go on vacation, return, buy a new computer, and our programs were faster. This was magic.

Today, we're no longer under Moore&apos...

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