Mahout Overview

The Apache Mahout machine learning library's goal is to build scalable machine learning libraries.

Mahout currently has

  • User and Item based recommenders
  • Matrix factorization based recommenders
  • K-Means, Fuzzy K-Means clustering
  • Latent Dirichlet Allocation
  • Singular value decomposition
  • Logistic regression based classifier
  • Complementary Naive Bayes classifier
  • Random forest decision tree based classifier
  • High performance java collections (previously colt collections)
  • A vibrant community
With scalable we mean:
Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms
Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license.
Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more.
Currently Mahout supports mainly three use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category.

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