Group: org.apache.mahout - All Dependencies

Mahout Core · High performance scientific and technical computing data structures and methods, mostly based on CERN's Colt Java API

Mahout Math · High performance scientific and technical computing data structures and methods, mostly based on CERN's Colt Java API

Mahout Collections · Primitive-type collections based on CERN's Colt Java API

Mahout Utilities · Utilities for preparing content into formats for Mahout.

Mahout Math Scala bindings · High performance scientific and technical computing data structures and methods, mostly based on CERN's Colt Java API

- Mahout HDFS Support · Scalable machine learning libraries

Mahout Examples · Scalable machine learning library examples

Mahout Map-Reduce · Scalable machine learning libraries

Mahout Integration · Optional components of Mahout which generally support interaction with third party systems, formats, APIs, etc.

Mahout Spark bindings · Mahout Bindings for Apache Spark

Mahout Spark bindings shell · Mahout Bindings for Apache Spark

Mahout H2O backend · H2O Backend for Mahout DSL

Mahout Flink bindings · Mahout Bindings for Apache Flink

- Mahout Spark Engine · Mahout Bindings for Apache Spark

Mahout Taste Webapp · Mahout Taste Collaborative Filtering Web App

Apache Mahout · Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classification and batch based 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 four 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 existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

Mahout Distribution · Distribution Package

Mahout Native VienniaCL OpenCL Bindings · Native Structures and interfaces to be used from Mahout math-scala.

Mahout Native VienniaCL OpenMP Bindings · Native Structures and interfaces to be used from Mahout math-scala.

Mahout Engine · Parent of Various Engines on which you can run Mahout.

Mahout Math/Scala wrappers · High performance scientific and technical computing data structures and methods, mostly based on CERN's Colt Java API