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Learning Apache Mahout
Learning Apache Mahout

Learning Apache Mahout: Acquire practical skills in Big Data Analytics and explore data science with Apache Mahout

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Learning Apache Mahout

Chapter 1. Introduction to Mahout

Mahout is an open source machine learning library from Apache. Mahout primarily implements clustering, recommender engines (collaborative filtering), classification, and dimensionality reduction algorithms but is not limited to these.

The aim of Mahout is to provide a scalable implementation of commonly used machine learning algorithms. Mahout is the machine learning tool of choice if the data to be used is large. What we generally mean by large is that the data cannot be processed on a single machine. With Big Data becoming an important focus area, Mahout fulfils the need for a machine learning tool that can scale beyond a single machine. The focus on scalability differentiates Mahout from other tools such as R, Weka, and so on.

The learning implementations in Mahout are written in Java, and major portions, but not all, are built upon Apache's Hadoop distributed computation project using the MapReduce paradigm. Efforts are on to build Mahout on Apache Spark using Scala DSL. Programs written in Scala DSL will be automatically optimized and executed in parallel on Apache Spark. Commits of new algorithms in MapReduce have been stopped and the existing MapReduce implementation will be supported.

The purpose of this chapter is to understand the fundamental concepts behind Mahout. In particular, we will cover the following topics:

  • Why Mahout

  • When Mahout

  • How Mahout

Why Mahout

We already have many good open source machine learning software tools. The statistical language R has a very large community, good IDE, and a large collection of machine learning packages already implemented. Python has a strong community and is multipurpose, and in Java we have Weka.

So what is the need for a new machine learning framework?

The answer lies in the scale of data. Organizations are generating terabytes of data daily and there is a need for a machine learning framework that can process that amount of data.

That begs a question, can't we just sample the data and use existing tools for our analytics use cases?

Simple techniques and more data is better

Collecting and processing data is much easier today than, say, a decade ago. IT infrastructure has seen an enormous improvement; ETL tools, click stream providers such as Google analytics, stream processing frameworks such as Kafka, Storm, and so on have made collecting data much easier. Platforms like Hadoop, Cassandra, and MPP databases such as Teradata have made storing and processing huge amount of data much easier than earlier. From a large-scale production algorithm standpoint, we have seen that simpler algorithms on very large amounts of data produce reasonably good results.

Sampling is difficult

Sampling may lead to over-fitting and increases the complexity of preparing data to build models to solve the problem at hand. Though sampling tends to simplify things by allowing scientists to work on a small sample instead of the whole population and helps in using existing tools like R to scale up to the task, getting a representative sample is tricky.

I'd say when you have the choice of getting more data, take it. Never discard data. Throw more (commodity) hardware at the data by using platforms and tools such as Hadoop and Mahout.

Community and license

Another advantage of Mahout is its license. Mahout is Apache licensed, which means that you can incorporate pieces of it into your own software regardless of whether you want to release your source code. However, other ML software, such as Weka, are under the GPL license, which means that incorporating them into your software forces you to release source code for any software you package with Weka components.

When Mahout

We have discussed the advantages of using Mahout, let's now discuss the scenarios where using Mahout is a good choice.

Data too large for single machine

If the data is too large to process on a single machine then it would be a good starting point to think about a distributed system. Rather than scaling and buying bigger hardware, it could be a better option to scale out, buy more machines, and distribute the processing.

Data already on Hadoop

A lot of enterprises have adopted Hadoop as their Big Data platform and have used it to store and aggregate data. Mahout has been designed to run algorithms on top of Hadoop and has a relatively straightforward configuration.

If your data or the bulk of it is already on Hadoop, then Mahout is a natural choice to run machine learning algorithms.

Algorithms implemented in Mahout

Do check whether the use case that needs to be implemented has a corresponding algorithm implemented in Mahout, or you have the required expertise to extend Mahout to implement your own algorithms.

How Mahout

In this section, you will learn how to install and configure Mahout.

Setting up the development environment

For any development work involving Mahout, and to follow the examples in this book, you will require the following setup:

  • Java 1.6 or higher

  • Maven 2.0 or higher

  • Hadoop 1.2.1

  • Eclipse with Maven plugin

  • Mahout 0.9

I prefer to try out the latest version, barring when there are known compatibility issues. To configure Hadoop, follow the instructions on this page We will focus on configuring Maven, Eclipse with the Maven plugin, and Mahout.

Configuring Maven

Maven can be downloaded from one of the mirrors of the Apache website We use Apache Maven 3.2.5 and the same can be downloaded using this command:

cd /usr/local
sudo tar xzf $HOME/Downloads/ /usr/local/apache-maven-3.2.5-bin.tar.gz
sudo mv apache-maven-3.2.5 maven
sudo chown -R $USER maven

Configuring Mahout

Mahout can be configured to be run with or without Hadoop. Currently, efforts are on to port Mahout on Apache Spark but it is in a nascent stage. We will discuss Mahout on Spark in Chapter 8, New Paradigm in Mahout. In this chapter, you are going to learn how to configure Mahout on top of Hadoop.

We will have two configurations for Mahout. The first we will use for practicing command line examples of Mahout and the other, compiled from source, will be used to develop Mahout code using Java API and Eclipse.

Though we can use one Mahout configuration, I will take this opportunity to discuss both approaches.

Download the latest Mahout version using one of the mirrors listed at the Apache Mahout website The current release version is mahout-distribution-0.9.tar.gz. After the download completes, the archive should be in the Downloads folder under the user's home directory. Type the following on the command line. The first command moves the shell prompt to the /usr/local directory:

cd /usr/local

Extract the downloaded file to the directory mahout-distribution-0.9.tar.gz under the /usr/local directory. The command tar is used to extract the archive:

sudo tar xzf $HOME/Downloads/mahout-distribution-0.9.tar.gz

The third command mv renames the directory from mahout-distribution-0.9 to mahout:

sudo mv mahout-distribution-0.9 mahout

The last command chown changes the ownership of the file from the root user to the current user. The Linux command chown is used for changing the ownership of files and directories. The argument –R instructs the chown command to recursively change the ownership of subdirectories and $USER holds the value of the logged in user's username:

sudo chown -R $USER mahout

We need to update the .bashrc file to export the required variables and update the $PATH variable:

cd $HOME
vi .bashrc

At the end of the file, copy the following statements:

#Statements related to Mahout
export MAVEN_HOME=/usr/local/maven
export MAHOUT_HOME=/usr/local/mahout
###end of mahout statement

Exit from all existing terminals, start a new terminal, and enter the following command:

echo $PATH

Check whether the output has the path recently added to Maven and Mahout.

Type the following commands on the command line; both commands should be recognized:

mvn –-version

Configuring Eclipse with the Maven plugin and Mahout

Download Eclipse from the Eclipse mirror mentioned on the home page. We have used Eclipse Kepler SR2 for this book. The downloaded archive should be in the Downloads folder of the user's home directory. Open a terminal and enter the following command:

cd /usr/local
sudo tar xzf $HOME/Downloads/eclipse-standard-kepler-SR2-linux-gtk-x86_64.tar.gz
sudo chown -R $USER eclipse

Go into the Eclipse directory and open up the Eclipse GUI. We will now install the Maven plugin. Click on Help then Eclipse Marketplace and then in the search panel type m2e and search. Once the search results are displayed hit Install and follow the instructions. To complete the installation hit the Next button and press the Accept button whenever prompted. Once the installation is done, Eclipse will prompt for a restart. Hit OK and let Eclipse restart.

Now to add Mahout dependency to any Maven project we need, add the following dependency in the pom.xml file:


Eclipse will download and add all the dependencies.

Now we should import the code repository of this book to Eclipse. Open Eclipse and follow the following sequence of steps. The pom.xml file has all the dependencies included in it and Eclipse will download and resolve the dependencies.

Go to File | Import | Maven | Existing Maven Projects | Next | Browse to the location of the source folder that comes with this book | Finish.


Downloading the example code

You can download the example code files from your account at for all the Packt Publishing books you have purchased. If you purchased this book elsewhere, you can visit and register to have the files e-mailed directly to you.

Mahout command line

Mahout provides an option for the command line execution of machine learning algorithms. Using the command line, an initial prototype of the model can be built quickly.

A few examples of command line are discussed. A great place to start is to go through Mahout's example scripts, the example scripts; are located under the Mahout home folder in the examples folder:

cd examples/bin
ls --ltr

The Mahout example scripts are as follows:

Open the file README.txt in vi editor and read the description of the scripts. We will be discussing them in the subsequent sections of this chapter:

vi README.txt

The description of the example script is as follows:


It is a good idea to try out a few command line Mahout algorithms before writing Mahout Java code. This way we can shortlist a few algorithms that might work on the given data and problem, and save a lot of time.

A clustering example

In this section, we will discuss the command line implementation of clustering in Mahout and use the example script as reference.

On the terminal please type:


This script clusters the Reuters dataset using a variety of algorithms. It downloads the dataset automatically, parses and copies it to HDFS (Hadoop Distributed File System), and based upon user input, runs the corresponding clustering algorithm.

On the vi terminal type the command:

:set number

This will display the line numbers of the lines in the file. The algorithms implemented are kmeans, fuzzykmeans, lda, and streamingkmeans; line 42 of the code has a list of all algorithms implemented in the script:

algorithm=( kmeansfuzzykmeansldastreamingkmeans) #A list of all algorithms implemented in the script

Input is taken from the user in line 51 by the read statement:

read -p "Enter your choice : " choice

Line 57 sets the temp working directory variable:


On line 79, the curl statement downloads the Reuters data to the working directory, first checking whether the file is already present in the working directory between lines 70 to 73:

curl -o ${WORK_DIR}/reuters21578.tar.gz

From line 89, the Reuters tar is extracted to the reuters-sgm folder under the working directory:

tar xzf ${WORK_DIR}/reuters21578.tar.gz -C ${WORK_DIR}/reuters-sgm
Reuter's raw data file

Let's have a look at one of the raw files. Open the reut2-000.sgm file in a text editor such as vi or gedit.

The Reuter's raw file looks like this:

The Reuters data is distributed in 22 files, each of which contains 1,000 documents, except for the last (reut2-021.sgm), which contains 578 documents. The files are in the SGML (standard generalized markup language) format, which is similar to XML. The SGML file needs to be parsed.

On line 93, the Reuters data is parsed using Lucene. Lucene has built-in classes and functions to process different file formats. The logic of parsing the Reuters dataset is implemented in the ExtractReuters class. The SGML file is parsed and the text elements are extracted from it.


Apache Lucene is a free/open source information retrieval software library.

We will use the ExtractReuters class to extract the sgm file to text format.

$MAHOUT org.apache.lucene.benchmark.utils.ExtractReuters ${WORK_DIR}/reuters-sgm ${WORK_DIR}/reuters-out

Now let's look at the Reuters processed file. The following figure is a snapshot taken from the text file extracted from the sgm files we saw previously by the ExtractReuters class:

On lines 95 to 101, data is loaded from a local directory to HDFS, deleting the reuters-sgm and reuters-out folders if they already exist:

  echo "Copying Reuters data to Hadoop"
  $HADOOP dfs -rmr ${WORK_DIR}/reuters-sgm
  $HADOOP dfs -rmr ${WORK_DIR}/reuters-out
  $HADOOP dfs -put ${WORK_DIR}/reuters-sgm ${WORK_DIR}/reuters-sgm
  $HADOOP dfs -put ${WORK_DIR}/reuters-out ${WORK_DIR}/reuters-out

On line 105, the files are converted into sequence files. Mahout works with sequence files.


Sequence files are the standard input of Mahout machine learning algorithms.

$MAHOUT seqdirectory -i ${WORK_DIR}/reuters-out -o ${WORK_DIR}/reuters-out-seqdir -c UTF-8 -chunk 64 -xm sequential

On lines 109 to 111, the sequence file is converted to a vector representation. Text needs to be converted into a vector representation so that a machine learning algorithm can process it. We will talk about text vectorization in details in Chapter 10, Case Study – Text Analytics.

 $MAHOUT seq2sparse -i ${WORK_DIR}/reuters-out-seqdir/ 
  -o ${WORK_DIR}/reuters-out-seqdir-sparse-kmeans --maxDFPercent 85 – namedVector

From here on, we will only explain the k-means algorithm execution; we encourage you to read and understand the other three implementations too. A detailed discussion of clustering will be covered in Chapter 7, Clustering with Mahout.

Clustering is the process of partitioning a bunch of data points into related groups called clusters. K-means clustering partitions a dataset into a specified number of clusters by minimizing the distance between each data point and the center of the cluster using a distance metric. A distance metric is a way to define how far or near a data point is from another. K-means requires users to provide the number of clusters and optionally user-defined cluster centroids.

To better understand how data points are clustered together, please have a look at the sample figure displaying three clusters. Notice that the points that are nearby are grouped together into three distinct clusters. A few points don't belong to any clusters, those points represent outliers and should be removed prior to clustering.

Here is an example of the command line for k-means clustering:



--input (-i)

This is the path to the job input directory.

--clusters (-c)

These are the input centroids and they must be a SequenceFile of type Writable or Cluster/Canopy. If k is also specified, then a random set of vectors will be selected and written out to this path first.

--output (-o)

This is the directory pathname for the output.


This is the class name of DistanceMeasure; the default is SquaredEuclidean.


This is the convergence delta value; the default is 0.5.

--maxIter (-x)

This is the maximum number of iterations.

--maxRed (-r)

This is the number of reduce tasks; this defaults to 2.

--k (-k)

This is the k in k-means. If specified, then a random selection of k vectors will be chosen as the Centroid and written to the cluster's input path.

--overwrite (-ow)

If this is present, overwrite the output directory before running the job.

--help (-h)

This prints out Help.

--clustering (-cl)

If this is present, run clustering after the iterations have taken place.

Lines 113 to 118 take the sparse matrix and runs the k-means clustering algorithm using the cosine distance metric. We pass –k the number of clusters as 20 and –x the maximum number of iterations as 10:

     $MAHOUT kmeans \
    -i ${WORK_DIR}/reuters-out-seqdir-sparse-kmeans/tfidf-vectors/ \
    -c ${WORK_DIR}/reuters-kmeans-clusters \
    -o ${WORK_DIR}/reuters-kmeans \
    -dm org.apache.mahout.common.distance.CosineDistanceMeasure \
    -x 10 -k 20 -ow --clustering \

Lines 120 to 125 take the cluster dump utility, read the clusters in sequence file format, and convert them to text files:

 $MAHOUT clusterdump \
    -i ${WORK_DIR}/reuters-kmeans/clusters-*-final \
    -o ${WORK_DIR}/reuters-kmeans/clusterdump \
    -d ${WORK_DIR}/reuters-out-seqdir-sparse-kmeans/dictionary.file-0 \
    -dt sequencefile -b 100 -n 20 --evaluate -dm org.apache.mahout.common.distance.CosineDistanceMeasure -sp 0 \ 
    --pointsDir ${WORK_DIR}/reuters-kmeans/clusteredPoints \
&& \    
  cat ${WORK_DIR}/reuters-kmeans/clusterdump

The clusterdump utility outputs the center of each cluster and the top terms in the cluster. A sample of the output is shown here:

A classification example

In this section, we will discuss the command line implementation of classification in Mahout and use the example script as a reference.

Classification is the task of identifying which set of predefined classes a data point belongs to. Classification involves training a model with a labeled (previously classified) dataset and then predicting new unlabeled data using that model. The common workflow for a classification problem is:

  1. Data preparation

  2. Train model

  3. Test model

  4. Performance measurement

Repeat steps until the desired performance is achieved, or the best possible solution is achieved or the project's time is up.

On the terminal, please type:


On the vi terminal, type the following command to show the line numbers for lines in the script:

:set number

The algorithms implemented in the script are cnaivebayes, naivebayes, sgd, and a last option clean, which cleans up the work directory

Line 44 creates a working directory for the dataset and all input/output:

export WORK_DIR=/tmp/mahout-work-${USER}

Lines 64 to 74 download and extract the 20news-bydate.tar.gz file after making sure it is not already downloaded:

  if [ ! -e ${WORK_DIR}/20news-bayesinput ]; then
    if [ ! -e ${WORK_DIR}/20news-bydate ]; then
      if [ ! -f ${WORK_DIR}/20news-bydate.tar.gz ]; then
        echo "Downloading 20news-bydate"
        curl -o ${WORK_DIR}/20news-bydate.tar.gz
      mkdir -p ${WORK_DIR}/20news-bydate
      echo "Extracting..."
      cd ${WORK_DIR}/20news-bydate && tar xzf ../20news-bydate.tar.gz && cd ..&&cd ..

The 20 newsgroups dataset consists of messages, one per file. Each file begins with header lines that specify things such as who sent the message, how long it is, what kind of software was used, and the subject. A blank line follows, and then the message body follows as unformatted text.

Lines 90 to 101 prepare the directory and copy the data to the Hadoop directory:

  echo "Preparing 20newsgroups data"
  rm -rf ${WORK_DIR}/20news-all
  mkdir ${WORK_DIR}/20news-all
  cp -R ${WORK_DIR}/20news-bydate/*/* ${WORK_DIR}/20news-all

  if [ "$HADOOP_HOME" != "" ] && [ "$MAHOUT_LOCAL" == "" ] ; then
    echo "Copying 20newsgroups data to HDFS"
    set +e
    $HADOOP dfs -rmr ${WORK_DIR}/20news-all
    set -e
    $HADOOP dfs -put ${WORK_DIR}/20news-all ${WORK_DIR}/20news-all

A snapshot of the raw 20newsgroups data file is provided below.

Lines 104 to 106 convert the full 20 newsgroups dataset into sequence files:

$ mahout seqdirectory  -i ${WORK_DIR}/20news-all –o ${WORK_DIR}/20news-seq -ow

Lines 109 to 111 convert the sequence files to vectors calculating the term frequency and inverse document frequency. Term frequency and inverse document frequency are ways of representing text using numeric representation:

./bin/mahout seq2sparse \
-i ${WORK_DIR}/20news-seq \
-o ${WORK_DIR}/20news-vectors -lnorm -nv  -wt tfidf

Lines 114 to 118 split the preprocessed dataset into training and testing sets. The test set will be used to test the performance of the model trained using the training sets:

./bin/mahout split \
-i ${WORK_DIR}/20news-vectors/tfidf-vectors \
--trainingOutput ${WORK_DIR}/20news-train-vectors \
--testOutput ${WORK_DIR}/20news-test-vectors  \
--randomSelectionPct 40 --overwrite --sequenceFiles -xm sequential

Lines 120 to 125 train the classifier using the training sets:

./bin/mahout trainnb \
-i ${WORK_DIR}/20news-train-vectors -el \
-o ${WORK_DIR}/model \
-li ${WORK_DIR}/labelindex \
-ow $c

Lines 129 to 133 test the classifier using the test sets:

./bin/mahout testnb \
-i ${WORK_DIR}/20news-train-vectors\
-m ${WORK_DIR}/model \
-l ${WORK_DIR}/labelindex \
-ow -o ${WORK_DIR}/20news-testing $c

Mahout API – a Java program example

Though using Mahout from the command line is convenient, fast, and serves the purpose in many scenarios, learning the Mahout API is important too. The reason being, using the API gives you more flexibility in terms of creating your machine learning application, and not all algorithms can be easily called from the command line. Working with the Mahout API helps to understand the internals of a machine learning algorithm.

Mahout core JAR files have the implementation of the main machine learning classes and the Mahout examples JAR file has some example code and wrappers built around the Mahout core classes. It is worth spending time going through the documentation and getting an overall understanding. The documentation for the version you are using can be found in the Mahout installation directory.

The Mahout documentation directory looks like this:

We will now look at a Mahout code example. We will write a classification example in which we will train an algorithm to predict whether a client has subscribed to a term deposit. Classification refers to the process of labeling a particular instance or row to a particular predefined category, called a class label. The purpose of the following example is to give you a hang of the development using Mahout, Eclipse, and Maven.

The dataset

We will use the bank-additional-full.csv file present in the learningApacheMahout/data/chapter4 directory as the input for our example. First, let's have a look at the structure of the data and try to understand it. The following table shows various input variables along with their data types:

Column Name


Variable Type


Age of the client



Type of job, for example, entrepreneur, housemaid, or management



Marital status



Education level



Has the client defaulted on credit?



Does the client have housing loan?



Does the client have personal loan?



Contact communication type



Last contact month of year



Last contact day of the week



Last contact duration, in seconds



Number of contacts



Number of days that passed since last contact



Number of contacts performed before this campaign



outcome of the previous marketing campaign



Employment variation rate - quarterly indicator



Consumer price index - monthly indicator



Consumer confidence index - monthly indicator



Euribor 3 month rate - daily indicator



Number of employees - quarterly indicator



Has the client subscribed a term deposit


Based on many attributes of the customer, we try to predict the target variable y (has the client subscribed to a term deposit?), which has a set of two predefined values, Yes and No. We need to remove the header line to use the data.

We will use logistic regression to build the model; logistic regression is a statistical technique that computes the probability of an unclassified item belonging to a predefined class.

You might like to run the example with the code in the source code that ships with this book; I will explain the important steps in the following section. In Eclipse, open the code file from the package chapter4.logistic.src, which is present in the directory learningApacheMahout/src/main/java/chapter4/src/logistic in the code folder that comes with this book.

The first step is to identify the source and target folders:

String inputFile = "data/chapter4/train_data/input_bank_data.csv";
String outputFile = "data/chapter4/model";

Once we know where to get the data from, we need to tell the algorithm about how to interpret the data. We pass the column name and the corresponding column type; here, n denotes the numeric column and w, the categorical columns of the data:

List<String> predictorList =Arrays.asList("age","job","marital","education","default","housing","loan","contact","month","day_of_week","duration","campaign","pdays","previous","poutcome","emp.var.rate","cons.price.idx","cons.conf.idx","euribor3m","nr.employed");

List<String> typeList = Arrays.asList("n","w","w","w","w","w","w","w","w","w","n","n","n","n","w","n","n","n","n","n");

Set the classifier parameters. LogisticModelParameters is a wrapper class, in Mahout's example distribution, used to set the parameters for training logistic regression and to return the instance of a CsvRecordFactory:

LogisticModelParameters lmp = new LogisticModelParameters();
        int passes = 50;

We set the the target variable y to be used for training, the maximum number of target categories to be 2 (Yes, No), the number of features or columns in the data excluding the target variable (which is 20), and some other settings (which we will learn about later in this book). The variable passed has been given a value of 50, which means the maximum number of iteration over the data will be 50.

The CsvRecordFactory class returns an object to parse the CSV file based on the parameters passed. The LogisticModelParameters class takes care of passing the parameters to the constructor of CsvRecordFactory. We use the class RandomAccessSparseVector to encode the data into vectors and train the model using lr.train(targetValue, input):

CsvRecordFactory csv = lmp.getCsvRecordFactory();
lr = lmp.createRegression();
for (int pass = 0; pass < passes; pass++) {
                BufferedReader in = new BufferedReader(new FileReader(inputFile));


                String line = in.readLine();
                int lineCount = 2;
                while (line != null) {
                  Vector input = new RandomAccessSparseVector(lmp.getNumFeatures());
                  int targetValue = csv.processLine(line, input);

                  // update model
                  lr.train(targetValue, input);

                  line = in.readLine();

The output after running the code would be an equation denoting the logistic regression. Excerpts of the equation are copied here:

y ~ -97.230*age + -12.713*campaign + . . .

You will learn about logistic regression, how to interpret the equation, and how to evaluate the results in detail in Chapter 4, Classification with Mahout.

Parallel versus in-memory execution mode

Mahout has both parallel and in-memory execution for many machine learning algorithms. In-memory execution can be used when the data size is smaller or to try out different algorithms quickly without installing Hadoop. In-memory execution is restricted to one machine whereas the parallel are designed to run on different machines. The parallel execution is implemented over Hadoop using the MapReduce paradigm, and for parallel execution; we call the code via the driver class to run the Hadoop MapReduce job. Let's see which algorithms have single machine and parallel execution. We have grouped the algorithms according to the paradigm such as collaborative filtering, classification, and so on. The first column of the table is the name of the column, the second column indicates whether the algorithm has a single machine implementation, and the third column indicates whether the algorithm has a parallel execution implementation.

The collaborative filtering table is as follows:


Single machine


User-based collaborative filtering



Item-based collaborative filtering



Matrix factorization with alternating least squares



Matrix factorization with alternating least squares on implicit feedback



Weighted matrix factorization



The classification table is as follows:


Single machine


Logistic regression



Naïve Bayes/Complementary naïve Bayes



Random forest



Hidden Markov models



Multilayer perceptron



The clustering table is as follows:


Single machine


Canopy clustering



k-means clustering



Fuzzy k-means



Streaming k-means



Spectral clustering



The dimensionality reduction table is as follows:


Single machine


Singular value decomposition



Lanczos algorithm



Stochastic SVD



Principal component analysis



The topic models table is as follows:


Single machine


Latent Dirichlet allocation



The miscellaneous table is as follows:


Single machine


Frequent pattern mining













In this chapter, we discussed the guiding principle of Mahout and tried out some examples to get a hands-on feel of Mahout. We discussed why, when, and how to use Mahout and walked through the installation steps of the required tools and software. We then learned how to use Mahout from the command line and from the code, and finally concluded with a comparison between the parallel and the single-machine execution of Mahout.

This is the beginning of what will hopefully be an exciting journey. In the forthcoming chapters, we will discuss a lot of practical applications for Mahout. In the next chapter, we will discuss the core concepts of machine learning. A clear understanding of the concepts of different machine learning algorithms is of paramount importance for a successful career in data analytics.

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Key benefits

What you will learn

Configure Mahout on Linux systems and set up the development environment Become familiar with the Mahout command line utilities and Java APIs Understand the core concepts of machine learning and the classes that implement them Integrate Apache Mahout with newer platforms such as Apache Spark Solve classification, clustering, and recommendation problems with Mahout Explore frequent pattern mining and topic modeling, the two main application areas of machine learning Understand feature extraction, reduction, and the curse of dimensionality

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Table of Contents

17 Chapters
Learning Apache Mahout Chevron down icon Chevron up icon
Credits Chevron down icon Chevron up icon
About the Author Chevron down icon Chevron up icon
About the Reviewers Chevron down icon Chevron up icon Chevron down icon Chevron up icon
Preface Chevron down icon Chevron up icon
1. Introduction to Mahout Chevron down icon Chevron up icon
2. Core Concepts in Machine Learning Chevron down icon Chevron up icon
3. Feature Engineering Chevron down icon Chevron up icon
4. Classification with Mahout Chevron down icon Chevron up icon
5. Frequent Pattern Mining and Topic Modeling Chevron down icon Chevron up icon
6. Recommendation with Mahout Chevron down icon Chevron up icon
7. Clustering with Mahout Chevron down icon Chevron up icon
8. New Paradigm in Mahout Chevron down icon Chevron up icon
9. Case Study – Churn Analytics and Customer Segmentation Chevron down icon Chevron up icon
10. Case Study – Text Analytics Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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Where there is an eBook version of a title available, you can buy it from the book details for that title. Add either the standalone eBook or the eBook and print book bundle to your shopping cart. Your eBook will show in your cart as a product on its own. After completing checkout and payment in the normal way, you will receive your receipt on the screen containing a link to a personalised PDF download file. This link will remain active for 30 days. You can download backup copies of the file by logging in to your account at any time.

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Packt eBook and Licensing When you buy an eBook from Packt Publishing, completing your purchase means you accept the terms of our licence agreement. Please read the full text of the agreement. In it we have tried to balance the need for the ebook to be usable for you the reader with our needs to protect the rights of us as Publishers and of our authors. In summary, the agreement says:

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Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

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Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

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