Description:

Apache Mahout is an Apache Software Foundation project with the goal of creating both a community of users and a scalable, Java-based framework consisting of many machine learning algorithm implementations. The three major components of Mahout are an environment for building scalable algorithms, many new Scala + Spark and H2O (Apache Flink in progress) algorithms, and Mahout's mature Hadoop MapReduce algorithms.

Changes to previous version:

Apache Mahout introduces a new math environment we call Samsara, for its theme of universal renewal. It reflects a fundamental rethinking of how scalable machine learning algorithms are built and customized. Mahout-Samsara is here to help people create their own math while providing some off-the-shelf algorithm implementations. At its core are general linear algebra and statistical operations along with the data structures to support them. You can use is as a library or customize it in Scala with Mahout-specific extensions that look something like R. Mahout-Samsara comes with an interactive shell that runs distributed operations on a Spark cluster. This make prototyping or task submission much easier and allows users to customize algorithms with a whole new degree of freedom. Mahout Algorithms include many new implementations built for speed on Mahout-Samsara. They run on Spark 1.3+ and some on H2O, which means as much as a 10x speed increase. You’ll find robust matrix decomposition algorithms as well as a Naive Bayes classifier and collaborative filtering. The new spark-itemsimilarity enables the next generation of cooccurrence recommenders that can use entire user click streams and context in making recommendations.

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Arff, Lucene, Mahout Vector, Various, Cassandra, Hbase

Tags: Classification, Clustering, K Nearest Neighbor Classification, Genetic Algorithms, Collaborative Filtering, Collocations, Frequent Pattern Mining, Scalable Singular Value Decomposition, Svd, Machine L

Archive: download here

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