layout: default title: Mahout Wiki
Apache Mahout is a new Apache TLP project to create scalable, machine learning algorithms under the Apache license.
{toc:style=disc|minlevel=2}
Overview -- Mahout? What's that supposed to be?
Quickstart -- learn how to quickly setup Apache Mahout for your project.
FAQ -- Frequent questions encountered on the mailing lists.
Developer Resources -- overview of the Mahout development infrastructure.
How To Contribute -- get involved with the Mahout community.
How To Become A Committer -- become a member of the Mahout development community.
Hadoop -- several of our implementations depend on Hadoop.
Machine Learning Open Source Software -- other projects implementing Open Source Machine Learning libraries.
Mahout -- The name, history and its pronunciation
Who we are -- who are the developers behind Apache Mahout?
Books, Tutorials, Talks, Articles, News, Background Reading, etc. on Mahout
Issue Tracker -- see what features people are working on, submit patches and file bugs.
Source Code (SVN) -- [Fisheye|http://fisheye6.atlassian.com/browse/mahout] -- download the Mahout source code from svn.
Mailing lists and IRC -- links to our mailing lists, IRC channel and archived design and algorithm discussions, maybe your questions was answered there already?
Version Control -- where we track our code.
Powered By Mahout -- who is using Mahout in production?
Professional Support -- who is offering professional support for Mahout?
Mahout and Google Summer of Code -- All you need to know about Mahout and GSoC.
Glossary of commonly used terms and abbreviations
System Requirements -- what do you need to run Mahout?
Quickstart -- get started with Mahout, run the examples and get pointers to further resources.
Downloads -- a list of Mahout releases.
Download and installation -- build Mahout from the sources.
Mahout on Amazon's EC2 Service -- run Mahout on Amazon's EC2.
Mahout on Amazon's EMR -- Run Mahout on Amazon's Elastic Map Reduce
Integrating Mahout into an Application -- integrate Mahout's capabilities in your application.
Matrix and Vector Needs -- requirements for Mahout vectors.
Learn more about mahout-collections , containers for efficient storage of primitive-type data and open hash tables.
Learn more about the Algorithms discussed and employed by Mahout.
Learn more about the Mahout recommender implementation .
This section describes tools that might be useful for working with Mahout.
Converting Content -- Mahout has some utilities for converting content such as logs to formats more amenable for consumption by Mahout. Creating Vectors -- Mahout's algorithms operate on vectors. Learn more on how to generate these from raw data. Viewing Result -- How to visualize the result of your trained algorithms.
Collections -- To try out and test Mahout's algorithms you need training data. We are always looking for new training data collections.
How to edit this Wiki
This Wiki is a collaborative site, anyone can contribute and share:
There are some conventions used on the Mahout wiki:
* {noformat}+*TODO:*+{noformat} (+*TODO:*+ ) is used to denote sections
that definitely need to be cleaned up. * {noformat}+Mahout_(version)+{noformat} (+Mahout_0.2+) is used to draw attention to which version of Mahout a feature was (or will be) added to Mahout.