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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

Large-scale clustering with Mahout

Mahout (http://mahout.apache.org/) is a machine learning library intended for use in distributed computing environments. Version 0.9 of the library targets Hadoop and is the version we'll be using here.

Note

At the time of writing, Mahout 0.10 has just been released and also targets Spark. Spark is an alternative distributed computing framework that we'll be introducing in the next chapter.

We saw in the previous chapter that one of Hadoop's abstractions is the sequence file: a binary representation of Java keys and values. Many of Mahout's algorithms expect to operate on sequence files, and we'll need to create one as input to Mahout's k-means algorithm. Mahout's k-means algorithm also expects to receive its input as a vector, represented by one of Mahout's vector types.

Although Mahout contains classes and utility programs that will extract vectors from text, we'll use this as an opportunity to demonstrate how...

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