Apache Mahout Framework

An Apache Mahout Framework is a software framework to build machine learning systems.

• Context:
  • It can (typically) contain a Mahout HMM Library.
  • …
• Example(s):
  • Mahout v0.13 (2017-)[1]
• See: Machine Learning Library, Hadoop Framework.

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References

• http://mahout.apache.org/

2017

• http://mahout.apache.org/release-notes/Apache-Mahout-0.13.0-Release-Notes.pdf
  • QUOTE: Mahout has historically focused on highly scalable algorithms, and since moving on from MapReduce-based jobs, Mahout now includes some Mahout-Samsara based implementations:
    • Distributed and in-core Stochastic Singular Value Decomposition (SSVD).
    • Distributed Principal Component Analysis (PCA).
    • Distributed and in-core QR Reduction (QR).
    • Distributed Alternating Least Squares (ALS).
    • Collaborative Filtering: Item and Row Similarity based on cooccurrence and supporting multimodal user actions
    • Distributed Naive Bayes Training and Classification

2011

• http://en.wikipedia.org/wiki/Apache_Mahout
  • Apache Mahout is an Apache project to produce free implementations of distributed or otherwise scalable machine learning algorithms on the Hadoop platform. Mahout is a work in progress; the number of implemented algorithms has grown quickly, but there are still various multivariate analytics algorithms missing.

    While Mahout's core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm, it does not restrict contributions to Hadoop based implementations. Contributions that run on a single node or on a non-Hadoop cluster are also welcomed. For example, the 'Taste' collaborative-filtering recommender component of Mahout was originally a separate project, and can run stand-alone without Hadoop. Integration with initiatives such as the Pregel-like Giraph are actively under discussion.

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