ver 1.3 (2014/12/23) Library 349     Package 18,754     Class 215,432     Method 1,817,401   
   Apache Mahout
The Apache Mahoutâ„¢ project's goal is to build a scalable machine learning library.

With scalable we mean:
Scalable to 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.
   Version 0.9
Binary mahout-distribution-0.9.tar.gz
Source mahout-distribution-0.9-src.tar.gz
API (Math) /library/277/mahout-distribution-0.9/docs/mahout-math/ Package 21, Class 504, Method 5,579
API (Integration) /library/277/mahout-distribution-0.9/docs/mahout-integration/ Package 25, Class 134, Method 752
API (Core) /library/277/mahout-distribution-0.9/docs/mahout-core/ Package 96, Class 637, Method 3,705

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