Apache Mahout User’s Guide

Apache Mahout is a powerful, scalable, and versatile machine learning library designed for distributed data processing. It offers a comprehensive set of algorithms for various tasks, including classification, clustering, recommendation, and pattern mining. Built on top of the Apache Hadoop ecosystem, Mahout leverages MapReduce and Spark to enable data processing on large-scale datasets.

In this User’s Guide, we provide an overview of Apache Mahout, its key features, and how to get started with using the library for your machine learning projects.

Key Features

  • Scalability: Apache Mahout is designed to handle large-scale data processing by leveraging the power of Hadoop and Spark, making it an excellent choice for big data machine learning projects.
  • Versatility: Mahout offers a wide range of machine learning algorithms, covering classification, clustering, recommendation, and more, ensuring that you have the right tools for your specific use case.
  • Extensibility: The library is easily extensible, allowing you to add custom algorithms and processing steps to meet your unique requirements.
  • Integration: Mahout seamlessly integrates with other components of the Hadoop ecosystem, such as HDFS and HBase, simplifying data storage and retrieval in your projects.

Getting Started

  1. Installation: We guide you through the process of installing Apache Mahout on your system, detailing the prerequisites and the steps required for a successful setup.
  2. Data Preparation: Learn how to prepare your data for processing with Mahout, including importing, preprocessing, and transforming your datasets.
  3. Algorithm Selection: We provide an overview of the available algorithms in Mahout, along with guidance on selecting the best algorithm for your specific problem.
  4. Model Training and Evaluation: Understand how to train, validate, and evaluate machine learning models using Mahout’s tools and best practices.
  5. Deployment: Explore various options for deploying your trained models, such as integrating with web services or embedding within your applications.

By following this User’s Guide, you will gain the necessary knowledge and skills to effectively leverage Apache Mahout for your machine learning projects, harnessing the power of big data processing to achieve better results.



Twenty Newsgroups

Random Forests

Partial Implementation

Breiman Example

Neural Network

Restricted Boltzmann Machines

Logistic Regression

Class Discovery


Bayesian Commandline

Wikipedia Classifier Example


Support Vector Machines

Hidden Markov Models

Locally Weighted Linear Regression


Bankmarketing Example


Using Mahout With Python Via Jpype

Perceptron And Winnow


Parallel Frequent Pattern Mining

Mr Map Reduce

Matrix And Vector Needs

Independent Component Analysis

Creating Vectors

System Requirements


Creating Vectors From Text

Mahout Collections



Svd Singular Value Decomposition

Tf Idf Term Frequency Inverse Document Frequency

Principal Components Analysis

Gaussian Discriminative Analysis


D Ssvd

D Als

Spark Naive Bayes

Intro Cooccurrence Spark

Recommender Overview

D Spca

D Qr

Clustering Of Synthetic Control Data

Canopy Commandline

Latent Dirichlet Allocation

Visualizing Sample Clusters

K Means Clustering

Spectral Clustering

Viewing Results

K Means Commandline

Viewing Result

Expectation Maximization


Llr Log Likelihood Ratio


Fuzzy K Means

Hierarchical Clustering

Canopy Clustering

Streaming K Means

Cluster Dumper

Clustering Seinfeld Episodes

Lda Commandline

Fuzzy K Means Commandline

Recommender First Timer Faq

Matrix Factorization

Recommender Documentation


Intro Itembased Hadoop

Userbased 5 Minutes

Intro Cooccurrence Spark

Intro Als Hadoop

In Core Reference

How To Build An App

Out Of Core Reference

Spark Internals

H2O Internals

Classify A Doc From The Shell



Play With Shell

Dimensional Reduction


Playing With Samsara Flink

Flink Internals