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 project currently has map-reduce enabled (via Apache Hadoop) implementations of several clustering algorithms (k-Means, Streaming k-Means, Mean-Shift, Fuzzy k-Means, Dirichlet, Canopy), Naïve Bayes and Complementary Naïve Bayes classifiers, Hidden Markov Models, Stochastic Gradient Descent, Latent Dirichlet Allocation, Frequent Patternset Mining, Random Decision Forests, distributed Singular Value Decomposition, distributed collocations, collaborative filtering and more. Mahout also has an extensive linear algebra, statistics, primitive Java collections and other tools available.

Changes to previous version:

Apache Mahout 0.8 contains, amongst a variety of performance improvements and bug fixes, an implementation of Streaming K-Means, deeper Lucene/Solr integration and new scalable recommender algorithms. For a full description of the newest release, see http://mahout.apache.org/.

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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