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, distributed Singular Value Decomposition, distributed collocations, 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:

Added distributed (Map/Reduce) Singular Value Decomposition and Map/Reduce collocations. New high performance collections and matrix/vector libraries (based on Colt with many enhancements). Many new utilities for converting content to Mahout format. See http://lucene.apache.org/mahout/#17+March+2010+-+Apache+Mahout+0.3+released for more details.

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Arff, Lucene, Mahout Vector

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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