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, Mean-Shift, Fuzzy k-Means, Dirichlet, Canopy), Naïve Bayes and Complementary Naïve Bayes classifiers, Latent Dirichlet Allocation, Frequent Patternset Mining, Random Decision Forests, collaborative filtering, as well as support for distributed evolutionary computing. We are also planning implementations of neural nets, expectation maximization, hierarchical clustering, Support Vector Machines, regression techniques, and Principal Component Analysis, amongst others.

Changes to previous version:

Focus on performance and cleanup of APIs on the way to a 1.0 release. Added several new algorithms (LDA, Frequent Patternset Mining, Random Decision Forests). See 0.2 release announcement: http://lucene.apache.org/mahout/index.html#17+Nov.+2009+-+Apache+Mahout+0.2+released

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Arff, Lucene, Mahout Vector

Tags: Lda, R, Classification, Clustering, Machine Learning, K Nearest Neighbor Classification, Genetic Algorithms, Mapreduce, Collaborative Filtering, Frequent Patternset Mining, Latent Dirichlet Allocation

Archive: download here

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